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{ "count": 238, "next": "https://memento.epfl.ch/api/v1/events/?format=api&limit=10&offset=50&ordering=event__start_time", "previous": "https://memento.epfl.ch/api/v1/events/?format=api&limit=10&offset=30&ordering=event__start_time", "results": [ { "id": 70951, "title": "Toward Intelligent Behavior in Macroscopic Active Matter", "slug": "toward-intelligent-behavior-in-macroscopic-active", "event_url": "https://memento.epfl.ch/event/toward-intelligent-behavior-in-macroscopic-active", "visual_url": "https://memento.epfl.ch/image/32339/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32339/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32339/max-size.jpg", "lang": "en", "start_date": "2026-07-06", "end_date": "2026-07-10", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/toward-intelligent-behavior-in-macroscopic-active-matter-1481\">https://www.cecam.org/workshop-details/toward-intelligent-behavior-in-macroscopic-active-matter-1481</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nActive matter has emerged as a central framework for understanding systems composed of self-driven units across scales, ranging from molecular motors and cytoskeletal filaments to animal groups and robotic swarms. Initially, many foundational models focused on macroscopic agents – such as flocks, swarms, and driven granular particles – where simple interaction rules give rise to rich collective phenomena. However, over the past two decades, much of the focus has shifted toward microscopic and mesoscopic active systems, especially in soft and biological matter, supported by the technological development of high-resolution imaging, force measurement, and microfabrication. These advances have driven a more refined theoretical understanding, connecting microscopic dynamics with hydrodynamic and continuum-scale descriptions, and have found applications in biophysics, material science, and cellular biology. <br>\r\nIn parallel, yet often semi-independently, active matter concepts have flourished in ecological and robotic systems. In these domains, the agents – be they insects, birds, autonomous vehicles, or soft robots – not only self-propel and interact, but also sense their environments, make decisions, and adapt their behavior. These systems extend the classical framework of active matter by incorporating elements of intelligence, information processing, and environmental feedback. Notably, such systems can operate far from equilibrium and exhibit coordinated behavior that seems tuned for functional outcomes – navigation, foraging, or collective decision-making.<br>\r\nThese trends point toward a convergence: macroscopic active matter systems capable of intelligent, adaptive, or programmable behavior. This includes both natural systems (e.g., flocking insects, social insects, animal herds) and artificial systems (e.g., modular robots, programmable matter, active granular agents). The interplay of self-propulsion, interaction rules, information exchange, learning or memory, and system-level feedback opens exciting new directions for both fundamental science and applications. Recent efforts in this space combine techniques from statistical physics, nonlinear dynamics, robotics, and machine learning.<br>\r\nHowever, the communities working on these different aspects of active matter – soft matter physicists, ecologists, roboticists, and complexity scientists – remain fragmented, with limited opportunity for sustained dialogue. Bridging these communities is essential to develop a shared language, identify unifying principles, and guide the development of new experimental platforms and theoretical frameworks.<br>\r\n<br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://doi.org/10.1038/s41586-024-08514-6\" target=\"_blank\">[1] F. Gu, B. Guiselin, N. Bain, I. Zuriguel, D. Bartolo, Nature, <strong>638</strong>, 112-119 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1126/scirobotics.aav7874\" target=\"_blank\">[2] A. Rafsanjani, K. Bertoldi, A. Studart, Sci. Robot., <strong>4</strong>, (2019)</a><br>\r\n<a href=\"https://doi.org/10.34133/cbsystems.0301\" target=\"_blank\">[3] J. Tirado, A. Parvaresh, B. Seyidoğlu, D. Bedford, J. Jørgensen, A. Rafsanjani, Cyborg. Bionic. Syst., <strong>6</strong>, (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s42254-021-00406-2\" target=\"_blank\">[4] J. O’Byrne, Y. Kafri, J. Tailleur, F. van Wijland, Nat. Rev. Phys., <strong>4</strong>, 167-183 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41567-022-01704-x\" target=\"_blank\">[5] P. Baconnier, D. Shohat, C. López, C. Coulais, V. Démery, G. Düring, O. Dauchot, Nat. Phys., <strong>18</strong>, 1234-1239 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41567-023-02028-0\" target=\"_blank\">[6] A. Cavagna, L. Di Carlo, I. Giardina, T. Grigera, S. Melillo, L. Parisi, G. Pisegna, M. Scandolo, Nat. Phys., <strong>19</strong>, 1043-1049 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1155/2013/987549\" target=\"_blank\">[7] M. Bischof, E. Del Giudice, Molecular Biology International, <strong>2013</strong>, 1-19 (2013)</a><br>\r\n<a href=\"https://doi.org/10.1098/rstb.2019.0377\" target=\"_blank\">[8] A. Deutsch, P. Friedl, L. Preziosi, G. Theraulaz, Phil. Trans. R. Soc. B, <strong>375</strong>, 20190377 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1038/ncomms5688\" target=\"_blank\">[9] N. Kumar, H. Soni, S. Ramaswamy, A. Sood, Nat. Commun., <strong>5</strong>, 4688 (2014)</a><br>\r\n<a href=\"https://doi.org/10.1111/j.1756-8765.2009.01028.x\" target=\"_blank\">[10] M. Moussaid, S. Garnier, G. Theraulaz, D. Helbing, Topics in Cognitive Science, <strong>1</strong>, 469-497 (2009)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevx.15.021050\" target=\"_blank\">[11] R. Bebon, J. Robinson, T. Speck, Phys. Rev. X, <strong>15</strong>, 021050 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1126/scirobotics.abo6140\" target=\"_blank\">[12] M. Ben Zion, J. Fersula, N. Bredeche, O. Dauchot, Sci. Robot., <strong>8</strong>, (2023)</a><br>\r\n<a href=\"https://doi.org/10.1103/physreve.110.014606\" target=\"_blank\">[13] J. Fersula, N. Bredeche, O. Dauchot, Phys. Rev. E, <strong>110</strong>, 014606 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1038/s42005-024-01540-w\" target=\"_blank\">[14] L. Caprini, A. Ldov, R. Gupta, H. Ellenberg, R. Wittmann, H. Löwen, C. Scholz, Commun. Phys., <strong>7</strong>, 52 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1098/rspb.2021.0275\" target=\"_blank\">[15] T. Lengronne, D. Mlynski, S. Patalano, R. James, L. Keller, S. Sumner, Proc. R. Soc. B., <strong>288</strong>, rspb.2021.0275 (2021)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevlett.75.1226\" target=\"_blank\">[16] T. Vicsek, A. Czirók, E. Ben-Jacob, I. Cohen, O. Shochet, Phys. Rev. Lett., <strong>75</strong>, 1226-1229 (1995)</a><br>\r\n<a href=\"https://doi.org/10.1360/nso/20240005\" target=\"_blank\">[17] L. Ning, H. Zhu, J. Yang, Q. Zhang, P. Liu, R. Ni, N. Zheng, NSO., <strong>3</strong>, 20240005 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1088/1361-648x/adebd3\" target=\"_blank\">[18] G. Volpe, N. Araújo, M. Guix, M. Miodownik, N. Martin, L. Alvarez, J. Simmchen, R. Leonardo, N. Pellicciotta, Q. Martinet, J. Palacci, W. Ng, D. Saxena, R. Sapienza, S. Nadine, J. Mano, R. Mahdavi, C. Beck Adiels, J. Forth, C. Santangelo, S. Palagi, J. Seok, V. Webster-Wood, S. Wang, L. Yao, A. Aghakhani, T. Barois, H. Kellay, C. Coulais, M. van Hecke, C. Pierce, T. Wang, B. Chong, D. Goldman, A. Reina, V. Trianni, G. Volpe, R. Beckett, S. Nair, R. Armstrong, J. Phys.: Condens. Matter, <strong>37</strong>, 333501 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1088/1361-648x/ab6348\" target=\"_blank\">[19] G. Gompper, R. Winkler, T. Speck, A. Solon, C. Nardini, F. Peruani, H. Löwen, R. Golestanian, U. Kaupp, L. Alvarez, T. Kiørboe, E. Lauga, W. Poon, A. DeSimone, S. Muiños-Landin, A. Fischer, N. Söker, F. Cichos, R. Kapral, P. Gaspard, M. Ripoll, F. Sagues, A. Doostmohammadi, J. Yeomans, I. Aranson, C. Bechinger, H. Stark, C. Hemelrijk, F. Nedelec, T. Sarkar, T. Aryaksama, M. Lacroix, G. Duclos, V. Yashunsky, P. Silberzan, M. Arroyo, S. Kale, J. Phys.: Condens. Matter, <strong>32</strong>, 193001 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1038/529016a\" target=\"_blank\">[20] G. Popkin, Nature, <strong>529</strong>, 16-18 (2016)</a></p>", "image_description": "", "creation_date": "2026-01-26T14:57:51", "last_modification_date": "2026-01-26T16:42:52", "link_label": "Toward Intelligent Behavior in Macroscopic Active Matter", "link_url": "https://www.cecam.org/workshop-details/toward-intelligent-behavior-in-macroscopic-active-matter-1481", "canceled": "False", "cancel_reason": "", "place_and_room": "BCH 2103", "url_place_and_room": "https://plan.epfl.ch/?room==BCH%202103", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Wylie Ahmed, </strong>CNRS ; <strong>Laura Alvarez, </strong>University of Bordeaux ; <strong>Lorenzo Caprini, </strong>Heinrich-Heine University of Duesseldorf ; <strong>Matteo Paoluzzi, </strong>Sapienza University of Rome", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119442/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 70952, "title": "Multi-scale and multi-purpose simulations of DNA: the importance of data", "slug": "multi-scale-and-multi-purpose-simulations-of-dna-t", "event_url": "https://memento.epfl.ch/event/multi-scale-and-multi-purpose-simulations-of-dna-t", "visual_url": "https://memento.epfl.ch/image/32340/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32340/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32340/max-size.jpg", "lang": "en", "start_date": "2026-08-26", "end_date": "2026-08-28", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/multi-scale-and-multi-purpose-simulations-of-dna-the-importance-of-data-1484\">https://www.cecam.org/workshop-details/multi-scale-and-multi-purpose-simulations-of-dna-the-importance-of-data-1484</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nDNA is a dramatic example of a multiscale system, where Å-scale details impact the global properties of a meter-long fiber and where femtosecond processes can impact on the entire genome years later. This implies that any theoretical study on DNA should take into consideration the vast variety of space and time scales, making it necessary the adoption of multi-physics approaches, covering the entire range of theoretical methods from quantum chemistry to rough mesoscopic models. Within this scenario the importance of data to bias simulations and as a reference to calibrate low resolution methods (Dans et al. 2017; Neguembor et al. 2022; Schultz et al. 2025).<br>\r\nLarge efforts have been made to develop accurate low level DFT and semiempirical methods that can be data-providers for a new generation of force-field, as well as integrated in QM/MM packages for an efficient representation of DNA reactivity (Aranda et al. 2019). Atomistic force-field have gained accuracy, showing good ability to reproduce unusual forms of DNA and long segments of DNA in the context of chromatin (Collepardo-Guevara et al. 2015; Genna et al. 2025) and providing very useful data for the calibration of lower level coarse-grained or mesoscopic methods(De Pablo 2011; Farré-Gil et al. 2024) ,which have gained sequence specificity, scalability and computational efficiency, allowing to simulate kilo-to-megabase fragments of DNA. Very remarkable efforts have been made to move up these methods to represent chromatin, which requires the introduction of biases derived from experimental data (MNAseq, chromosome conformation capture, and even static or dynamic pictures obtained by ultra-resolution microscopy, and others (Buitrago et al. 2019; Neguembor et al. 2022; Li and Schlick 2024)). This has opened the possibility to recover dynamic “base-pair” resolution pictures of chromatin and study aspects from local and global chromatin rearrangements to inter-play between effector proteins and nucleosomes, the impact of lesions in chromatin structure, and even the role of phase separation in defining local chromatin arrangements (Joseph et al. 2021; Liu et al. 2025; Park et al. 2025).<br>\r\nAs the target systems move from the small atomistic detail to the entire chromatin fiber, the community is broken into different sub-communities. This generates a risk of disconnection, which would lead to a waste of effort reformulating solutions to already solved problems, or ignoring the characteristic that a method should have to maintain coherence with more accurate models, or to scale to represent systems of real biological interest. This will be the main objective of this meeting, which will join a variety of sub-communities with a common interest: the DNA.<br>\r\n<br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://doi.org/10.1038/s41929-019-0290-y\" target=\"_blank\">[1] J. Aranda, M. Terrazas, H. Gómez, N. Villegas, M. Orozco, Nat. Catal., <strong>2</strong>, 544-552 (2019)</a><br>\r\n<a href=\"https://doi.org/10.1093/nar/gkz759\" target=\"_blank\">[2] D. Buitrago, L. Codó, R. Illa, P. de Jorge, F. Battistini, O. Flores, G. Bayarri, R. Royo, M. Del Pino, S. Heath, A. Hospital, J. Gelpí, I. Heath, M. Orozco, Nucleic Acids Research, <strong>47</strong>, 9511-9523 (2019)</a><br>\r\n<a href=\"https://doi.org/10.1021/jacs.5b04086\" target=\"_blank\">[3] R. Collepardo-Guevara, G. Portella, M. Vendruscolo, D. Frenkel, T. Schlick, M. Orozco, J. Am. Chem. Soc., <strong>137</strong>, 10205-10215 (2015)</a><br>\r\n<a href=\"https://doi.org/10.1093/nar/gkw1355\" target=\"_blank\">[4] P. Dans, I. Ivani, A. Hospital, G. Portella, C. González, M. Orozco, Nucleic. Acids. Res., gkw1355 (2017)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-physchem-032210-103458\" target=\"_blank\">[5] J. de Pablo, Annu. Rev. Phys. Chem., <strong>62</strong>, 555-574 (2011)</a><br>\r\n<a href=\"https://doi.org/10.1093/nar/gkae444\" target=\"_blank\">[6] D. Farré-Gil, J. Arcon, C. Laughton, M. Orozco, Nucleic Acids Research, <strong>52</strong>, 6791-6801 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1093/nar/gkaf170\" target=\"_blank\">[7] V. Genna, G. Portella, A. Sala, M. Terrazas, I. Serrano-Chacón, J. González, N. Villegas, L. Mateo, C. Castellazzi, M. Labrador, A. Aviño, A. Hospital, A. Gandioso, P. Aloy, I. Brun-Heath, C. Gonzalez, R. Eritja, M. Orozco, Nucleic Acids Research, <strong>53</strong>, (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s43588-021-00155-3\" target=\"_blank\">[8] J. Joseph, A. Reinhardt, A. Aguirre, P. Chew, K. Russell, J. Espinosa, A. Garaizar, R. Collepardo-Guevara, Nat. Comput. Sci., <strong>1</strong>, 732-743 (2021)</a><br>\r\n<a href=\"https://doi.org/10.1093/nar/gkad1121\" target=\"_blank\">[9] Z. Li, T. Schlick, Nucleic Acids Research, <strong>52</strong>, 583-599 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.biochem.4c00737\" target=\"_blank\">[10] S. Liu, C. Wang, B. Zhang, Biochemistry, <strong>64</strong>, 1750-1761 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41594-022-00839-y\" target=\"_blank\">[11] M. Neguembor, J. Arcon, D. Buitrago, R. Lema, J. Walther, X. Garate, L. Martin, P. Romero, J. AlHaj Abed, M. Gut, J. Blanc, M. Lakadamyali, C. Wu, I. Brun Heath, M. Orozco, P. Dans, M. Cosma, Nat. Struct. Mol. Biol., <strong>29</strong>, 1011-1023 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41586-025-08971-7\" target=\"_blank\">[12] S. Park, R. Merino-Urteaga, V. Karwacki-Neisius, G. Carrizo, A. Athreya, A. Marin-Gonzalez, N. Benning, J. Park, M. Mitchener, N. Bhanu, B. Garcia, B. Zhang, T. Muir, E. Pearce, T. Ha, Nature, (2025)</a><br>\r\n<a href=\"https://doi.org/10.1002/wcms.70024\" target=\"_blank\">[13] E. Schultz, J. Kaplan, Y. Wu, S. Kyhl, R. Willett, J. de Pablo, WIREs. Comput. Mol. Sci., <strong>15</strong>, (2025)</a></p>", "image_description": "", "creation_date": "2026-01-26T15:07:17", "last_modification_date": "2026-02-09T10:46:40", "link_label": "Multi-scale and multi-purpose simulations of DNA: the importance of data", "link_url": "https://www.cecam.org/workshop-details/multi-scale-and-multi-purpose-simulations-of-dna-the-importance-of-data-1484", "canceled": "False", "cancel_reason": "", "place_and_room": "BCH 2103", "url_place_and_room": "https://plan.epfl.ch/?room==BCH%202103", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Juan J De Pablo</strong>, University of Chicago ; <strong>Adam Hospital</strong>, IRB Barcelona ; <strong>Modesto Orozco</strong>, IRB Barcelona", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119444/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 70954, "title": "SpectroDynamics 2026: Connecting Computational Spectroscopic Methods Across the Electromagnetic Spectrum", "slug": "spectrodynamics-2026-connecting-computational-sp-2", "event_url": "https://memento.epfl.ch/event/spectrodynamics-2026-connecting-computational-sp-2", "visual_url": "https://memento.epfl.ch/image/32342/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32342/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32342/max-size.jpg", "lang": "en", "start_date": "2026-09-07", "end_date": "2026-09-11", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/spectrodynamics-2026-connecting-computational-spectroscopic-methods-across-the-electromagnetic-spectrum-1489\">https://www.cecam.org/workshop-details/spectrodynamics-2026-connecting-computational-spectroscopic-methods-across-the-electromagnetic-spectrum-1489</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nLight provides one of the most detailed windows into molecules and matter. Modern light sources allow the probing of equilibrium and non-equilibrium phenomena with Å‐level spatial resolution and femto‐ to attosecond temporal precision. Advances in ultrafast laser technology, together with the rise of X-ray free‐electron lasers and next-generation synchrotron sources, have repeatedly pushed the boundaries of spectroscopic methods from low‐frequency collective modes in biomolecules to electronic and core‐level dynamics. An extensive toolbox of linear and multidimensional spectroscopic techniques now spans the entire electromagnetic spectrum. Terahertz (THz) pulses can coherently drive intermolecular and lattice vibrations in solids and soft matter [1], Mid‐IR and Raman methods map vibrational energy (re)distribution in liquids and vibrational signatures of individual modes in complex molecules [2]. Visible spectroscopy tracks ultrafast charge dynamics in chromophores [3] and photochemical molecular pathways [4], while X-ray sources from free-electron lasers and high-harmonic generation setups enabled time-resolved X-ray diffraction of gas‐phase [5] and condensed systems [6].<br>\r\nDespite sharing common scientific goals, the respective communities have traditionally operated in relative disconnection from each other, relying on different approximations, targeting different observables, and employing distinct numerical implementations. This disconnection manifests, among other symptoms, in the fact that schools, conferences, and workshops are often dedicated to a specific frequency window (e.g. IR spectroscopy) or to simulation methods targeting a class of specific processes (e.g. vibrational dynamics). Opportunities for dialogue and the building of a shared language are lacking. In fact, while preparing this proposal, it became evident that even foundational terms like ab initio or quantum dynamics carry different meanings across communities.<br>\r\nTo address this fragmentation, the proposed CECAM school brings together researchers from diverse backgrounds to foster mutual understanding and build lasting conceptual bridges. Over five days, participants will engage with both the theoretical foundations and practical implementations of spectroscopies across different communities. We will highlight the fact that despite their apparent differences, all spectroscopic methods can be traced back to a common starting point: a light–matter Hamiltonian that includes the quantum description of electronic, nuclear, and photonic degrees of freedom. From this unified framework, we will explore how different approximations—introduced at various stages—lead to the distinct theoretical approaches adopted in each field.<br>\r\nThe first part of the school will focus on approaches that solve the exact quantum molecular dynamics in reduced dimensionality. Within this framework, molecules are treated fully quantum-mechanically, while light is treated classically as an external perturbation within the dipole approximation. From the matter perspective, this means that the full electron + nuclear wavefunction is accessible, offering a great level of detail and information, and the accurate treatment of non-Born-Oppenheimer dynamics. From the light perspective, this means that spectroscopic signals are conveniently calculated via the response function approach (RFA) [7], which is however only valid in the weak field limit. Recently, the RFA has been used to design and simulate several spectroscopic signals of femtosecond molecular photochemistry using novel X-ray pulse sources [8], including stimulated X-ray Raman [9], transient X-ray absorption and transmission [10], and many others [11].<br>\r\nIn the second part, we will shift the focus to longer time scales with more degrees of freedom and study larger molecules in explicit environments (solvent, substrate, etc). In these cases, it is common practice to apply the Born-Oppenheimer approximation and take the classical limit for the nuclei, while keeping the electrons quantum, leading to (finite temperature) molecular dynamics (MD) approaches. To make these simulations computationally tractable, while retaining an explicit description of the electrons, electron–electron interactions are typically simplified using ground-state density functional theory (DFT). This approach, commonly referred to as ab initio molecular dynamics (AIMD), enables the simulation of vibrational spectroscopies such as infrared (IR) and Raman [12,13], as well as surface-specific techniques like sum-frequency generation (SFG) [14,15]. To access larger system sizes and longer simulation timescales, forces can be derived from classical interatomic potentials, facilitating the convergence of multidimensional spectroscopic observables such as THz-Raman spectra [16]. Alternatively, forces can be learned directly from first-principles data using machine-learning (ML) models, enabling ML-driven molecular dynamics and spectroscopy [17-21]. Through path integral techniques, the quantum nature of the nuclei can be recovered, which is particularly important for systems containing light atoms, such as hydrogen [22-24].<br>\r\nThe third part of the school will explore what happens when the primary interest shifts from vibrational to electronic dynamics. In this context, the electron dynamics at the DFT level can be incorporated by considering its time-dependent version (TDDFT), where the exchange-correlation functionals are usually adiabatic. With this method, UV-visible absorption [25], circular dichroism [26], inelastic X-ray scattering, and electron energy loss [27], and other spectroscopies can be computed. Finally, there are situations in which strong light-matter coupling demands an explicit treatment of the photons [28]. These can be reintroduced either by dressing the Kohn-Sham Hamiltonian with electron-photon exchange-correlation potentials (known as quantum-electrodynamics DFT, or QEDFT) [29] or by a semiclassical treatment of the photons solving Maxwell’s equations (the Maxwell-TDDFT method)[30]. These methods enable the calculation of spectra in cavities or arbitrary electromagnetic environments [31], and can account for polaritonic phenomena, radiative lifetimes, superradiance, and many more.<br>\r\nThis school brings together leading experts from exact quantum dynamics, ab initio MD, ML‐enabled simulations, and Maxwell–TDDFT to forge a common language and cross‐fertilize ideas. Lectures will cover both the fundamental principles and the latest advances in each area, highlighting current applications and open challenges. Complementing the lectures, hands-on tutorials will reinforce foundational concepts and provide important hands-on experience on several popular computational approaches (see hands-on section below).<br>\r\nBy spanning the electromagnetic spectrum and the hierarchy of theoretical methods, this school will equip PhD students and postdocs with a unified, multi‐scale, and inter-community perspective on quantum dynamics and spectroscopy. Participants will leave with both a solid grounding in foundational techniques and direct experience of the latest computational frontiers, ready to tackle open challenges in molecular and materials science.<br>\r\n<br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://doi.org/10.1063/1.4901216\" target=\"_blank\">[1] P. Hamm, The Journal of Chemical Physics, <strong>141</strong>, (2014)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jctc.3c00967\" target=\"_blank\">[2] M. Svendsen, K. Thygesen, A. Rubio, J. Flick, J. Chem. Theory Comput., <strong>20</strong>, 926-936 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevb.111.085114\" target=\"_blank\">[3] F. Bonafé, E. Albar, S. Ohlmann, V. Kosheleva, C. Bustamante, F. Troisi, A. Rubio, H. Appel, Phys. Rev. B, <strong>111</strong>, 085114 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1103/physreva.90.012508\" target=\"_blank\">[4] M. Ruggenthaler, J. Flick, C. Pellegrini, H. Appel, I. Tokatly, A. Rubio, Phys. Rev. A, <strong>90</strong>, 012508 (2014)</a><br>\r\n<a href=\"https://doi.org/10.1021/acsphotonics.9b00768\" target=\"_blank\">[5] J. Flick, D. Welakuh, M. Ruggenthaler, H. Appel, A. Rubio, ACS Photonics, <strong>6</strong>, 2757-2778 (2019)</a><br>\r\n<a href=\"https://doi.org/10.1063/1.3503594\" target=\"_blank\">[6] A. Sakko, A. Rubio, M. Hakala, K. Hämäläinen, The Journal of Chemical Physics, <strong>133</strong>, (2010)</a><br>\r\n<a href=\"https://doi.org/10.1039/b903200b\" target=\"_blank\">[7] D. Varsano, L. Espinosa-Leal, X. Andrade, M. Marques, R. di Felice, A. Rubio, Phys. Chem. Chem. Phys., <strong>11</strong>, 4481 (2009)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevb.54.4484\" target=\"_blank\">[8] K. Yabana, G. Bertsch, Phys. Rev. B, <strong>54</strong>, 4484-4487 (1996)</a><br>\r\n<a href=\"https://doi.org/10.1039/c9fd00056a\" target=\"_blank\">[9] Y. Litman, J. Behler, M. Rossi, Faraday Discuss., <strong>221</strong>, 526-546 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-physchem-090722-124705\" target=\"_blank\">[10] S. Althorpe, Annual Review of Physical Chemistry, <strong>75</strong>, 397-420 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.chemrev.5b00674\" target=\"_blank\">[11] M. Ceriotti, W. Fang, P. Kusalik, R. McKenzie, A. Michaelides, M. Morales, T. Markland, Chem. Rev., <strong>116</strong>, 7529-7550 (2016)</a><br>\r\n<a href=\"https://doi.org/10.1039/c7sc02267k\" target=\"_blank\">[12] M. Gastegger, J. Behler, P. Marquetand, Chem. Sci., <strong>8</strong>, 6924-6935 (2017)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpca.1c10417\" target=\"_blank\">[13] R. Han, R. Ketkaew, S. Luber, J. Phys. Chem. A, <strong>126</strong>, 801-812 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpclett.3c00398\" target=\"_blank\">[14] K. Inoue, Y. Litman, D. Wilkins, Y. Nagata, M. Okuno, J. Phys. Chem. Lett., <strong>14</strong>, 3063-3068 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpclett.8b00133\" target=\"_blank\">[15] T. Morawietz, O. Marsalek, S. Pattenaude, L. Streacker, D. Ben-Amotz, T. Markland, J. Phys. Chem. Lett., <strong>9</strong>, 851-857 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpclett.3c01989\" target=\"_blank\">[16] Y. Litman, J. Lan, Y. Nagata, D. Wilkins, J. Phys. Chem. Lett., <strong>14</strong>, 8175-8182 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1364/aop.8.000401\" target=\"_blank\">[17] D. Nicoletti, A. Cavalleri, Adv. Opt. Photon., <strong>8</strong>, 401 (2016)</a><br>\r\n<a href=\"https://doi.org/10.1063/1.4931106\" target=\"_blank\">[18] T. Ohto, K. Usui, T. Hasegawa, M. Bonn, Y. Nagata, The Journal of Chemical Physics, <strong>143</strong>, (2015)</a><br>\r\n<a href=\"https://doi.org/10.1021/jz301858g\" target=\"_blank\">[19] M. Sulpizi, M. Salanne, M. Sprik, M. Gaigeot, J. Phys. Chem. Lett., <strong>4</strong>, 83-87 (2012)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpclett.7b00391\" target=\"_blank\">[20] O. Marsalek, T. Markland, J. Phys. Chem. Lett., <strong>8</strong>, 1545-1551 (2017)</a><br>\r\n<a href=\"https://doi.org/10.1021/ct2000952\" target=\"_blank\">[21] C. Zhang, D. Donadio, F. Gygi, G. Galli, J. Chem. Theory Comput., <strong>7</strong>, 1443-1449 (2011)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-physchem-062322-051532\" target=\"_blank\">[22] D. Keefer, S. Cavaletto, J. Rouxel, M. Garavelli, H. Yong, S. Mukamel, Annu. Rev. Phys. Chem., <strong>74</strong>, 73-97 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jctc.3c00062\" target=\"_blank\">[23] S. Cavaletto, Y. Nam, J. Rouxel, D. Keefer, H. Yong, S. Mukamel, J. Chem. Theory Comput., <strong>19</strong>, 2327-2339 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1073/pnas.2015988117\" target=\"_blank\">[24] D. Keefer, T. Schnappinger, R. de Vivie-Riedle, S. Mukamel, Proc. Natl. Acad. Sci. U.S.A., <strong>117</strong>, 24069-24075 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.chemrev.7b00081\" target=\"_blank\">[25] M. Kowalewski, B. Fingerhut, K. Dorfman, K. Bennett, S. Mukamel, Chem. Rev., <strong>117</strong>, 12165-12226 (2017)</a><br>\r\n[26] Shaul Mukamel, Principles of nonlinear optical spectroscopy, Oxford University Press, New York 1995<br>\r\n<a href=\"https://doi.org/10.1038/s41586-020-2417-3\" target=\"_blank\">[27] J. Kim, S. Nozawa, H. Kim, E. Choi, T. Sato, T. Kim, K. Kim, H. Ki, J. Kim, M. Choi, Y. Lee, J. Heo, K. Oang, K. Ichiyanagi, R. Fukaya, J. Lee, J. Park, I. Eom, S. Chun, S. Kim, M. Kim, T. Katayama, T. Togashi, S. Owada, M. Yabashi, S. Lee, S. Lee, C. Ahn, D. Ahn, J. Moon, S. Choi, J. Kim, T. Joo, J. Kim, S. Adachi, H. Ihee, Nature, <strong>582</strong>, 520-524 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevlett.114.255501\" target=\"_blank\">[28] M. Minitti, J. Budarz, A. Kirrander, J. Robinson, D. Ratner, T. Lane, D. Zhu, J. Glownia, M. Kozina, H. Lemke, M. Sikorski, Y. Feng, S. Nelson, K. Saita, B. Stankus, T. Northey, J. Hastings, P. Weber, Phys. Rev. Lett., <strong>114</strong>, 255501 (2015)</a><br>\r\n<a href=\"https://doi.org/10.1038/nature09346\" target=\"_blank\">[29] D. Polli, P. Altoè, O. Weingart, K. Spillane, C. Manzoni, D. Brida, G. Tomasello, G. Orlandi, P. Kukura, R. Mathies, M. Garavelli, G. Cerullo, Nature, <strong>467</strong>, 440-443 (2010)</a><br>\r\n<a href=\"https://doi.org/10.1039/d2fd00014h\" target=\"_blank\">[30] D. Brey, R. Binder, R. Martinazzo, I. Burghardt, Faraday Discuss., <strong>237</strong>, 148-167 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.chemrev.9b00813\" target=\"_blank\">[31] C. Baiz, B. Błasiak, J. Bredenbeck, M. Cho, J. Choi, S. Corcelli, A. Dijkstra, C. Feng, S. Garrett-Roe, N. Ge, M. Hanson-Heine, J. Hirst, T. Jansen, K. Kwac, K. Kubarych, C. Londergan, H. Maekawa, M. Reppert, S. Saito, S. Roy, J. Skinner, G. Stock, J. Straub, M. Thielges, K. Tominaga, A. Tokmakoff, H. Torii, L. Wang, L. Webb, M. Zanni, Chem. Rev., <strong>120</strong>, 7152-7218 (2020)</a></p>", "image_description": "", "creation_date": "2026-01-26T15:20:44", "last_modification_date": "2026-01-26T16:44:05", "link_label": "SpectroDynamics 2026: Connecting Computational Spectroscopic Methods Across the Electromagnetic Spec", "link_url": "https://www.cecam.org/workshop-details/spectrodynamics-2026-connecting-computational-spectroscopic-methods-across-the-electromagnetic-spectrum-1489", "canceled": "False", "cancel_reason": "", "place_and_room": "BCH 2103", "url_place_and_room": "https://plan.epfl.ch/?room==BCH%202103", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Franco Bonafé</strong>, Max Planck Institute for the Structure and Dynamics of Matter ; <strong>Daniel Keefer,</strong> Max Planck Institute for Polymer Research ; <strong>Yair Litman</strong>, Max Planck Institute for Polymer Research", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119447/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 70955, "title": "Bridging Biomolecular Simulations and Experiments Across Time and Length Scales: from Single Molecules to Entire Organelles", "slug": "bridging-biomolecular-simulations-and-experiments", "event_url": "https://memento.epfl.ch/event/bridging-biomolecular-simulations-and-experiments", "visual_url": "https://memento.epfl.ch/image/32343/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32343/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32343/max-size.jpg", "lang": "en", "start_date": "2026-09-14", "end_date": "2026-09-17", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/bridging-biomolecular-simulations-and-experiments-across-time-and-length-scales-from-single-molecules-to-entire-organelles-1493\">https://www.cecam.org/workshop-details/bridging-biomolecular-simulations-and-experiments-across-time-and-length-scales-from-single-molecules-to-entire-organelles-1493</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nMolecular simulations are firmly established as a central tool in the life sciences over the last few decades. This is evident from the now-standard use of molecular dynamics simulations by molecular biologists and biophysicists, and the remarkable success of AlphaFold, which has convinced even the most skeptical of the critical role of these methods in contemporary biological research.<br>\r\nHowever, new challenges are emerging. It is increasingly apparent that to understand biomolecular function, we must move beyond studying isolated molecules. The focus is now shifting towards examining large, dynamic complexes of biomolecules within their complex native environments, complete with post-translational modifications. Embracing this complexity is crucial for understanding how biological functions and cellular structures emerge and adapt.<br>\r\nThis workshop will address existing and emerging frontiers, discussing both current challenges and the future of molecular simulations needed to meet them. It will gather simulation experts that have been actively developing methods that can increase simulation accuracy and extend their applicability range across multiple scales, as well as experimentalists performing advanced studies that can address outstanding challenges occurring at computationally accessible time and length scales. A main aim will be to discuss how to improve the accuracy of simulations, integrate simulations and cutting-edge experiments, and how to best take advantage of innovative enhanced sampling and machine learning-based approaches.<br>\r\nThe workshop will seize the opportunity to celebrate the outstanding scientific achievements of Gerhard Hummer, a prominent leader in the field, on his sixty’s birthday. Many of the participants that have already expressed their intention to attend and support the workshop or past or current theoretical and experimental scientist that have been either collaborators and co-authors, mentored by, or inspired by Gerhard’s ideas and expertise.</p>", "image_description": "", "creation_date": "2026-01-26T15:39:35", "last_modification_date": "2026-01-26T16:44:37", "link_label": "Bridging Biomolecular Simulations and Experiments Across Time and Length Scales: from Single Molecul", "link_url": "https://www.cecam.org/workshop-details/bridging-biomolecular-simulations-and-experiments-across-time-and-length-scales-from-single-molecules-to-entire-organelles-1493", "canceled": "False", "cancel_reason": "", "place_and_room": "BCH 2103", "url_place_and_room": "https://plan.epfl.ch/?room==BCH%202103", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Nicolae-Viorel Buchete</strong>, University College Dublin ; <strong>Pilar Cossio</strong>, Flatiron Institute ; <strong>Roberto Covino</strong>, Goethe University Frankfurt -- Frankfurt Institute for Advanced Studies ; <strong>Ville Kaila</strong>, Stockholm University ; <strong>Edina Rosta</strong>, University College London", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119449/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api" ] }, { "id": 70956, "title": "G protein-coupled receptors functional dynamics revealed by experimental and computational structural data", "slug": "g-protein-coupled-receptors-functional-dynamics-re", "event_url": "https://memento.epfl.ch/event/g-protein-coupled-receptors-functional-dynamics-re", "visual_url": "https://memento.epfl.ch/image/32345/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32345/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32345/max-size.jpg", "lang": "en", "start_date": "2026-10-07", "end_date": "2026-10-09", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/g-protein-coupled-receptors-functional-dynamics-revealed-by-experimental-and-computational-structural-data-1488\">https://www.cecam.org/workshop-details/g-protein-coupled-receptors-functional-dynamics-revealed-by-experimental-and-computational-structural-data-1488</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nG protein-coupled receptors (GPCRs) represent a vast and diverse class of transmembrane proteins that orchestrate a wide range of physiological processes by responding to both endogenous and exogenous ligands [1,2]. These receptors are essential to critical functions such as metabolism, immune regulation, neuronal signaling, and sensory perception - including vision and olfaction. Due to their physiological relevance and membrane accessibility, GPCRs are the targets of approximately 34% of all prescribed medications, accounting for nearly 27% of the global pharmaceutical market [3]. <br>\r\nDespite their pharmaceutical importance, key aspects of GPCR function remain elusive. The canonical activation model posits that agonist binding to the extracellular orthosteric site triggers allosteric changes - most notably, the outward displacement of transmembrane helices 5 (TM5) and 6 (TM6) on the intracellular side - ultimately leading to receptor activation [2-4]. However, recent evidence suggests a more nuanced mechanism. In several GPCRs, activation appears to involve cooperative engagement between the agonist and the G protein. For example, the G protein may disrupt an \"inactivating ionic lock\" - a salt bridge between TM3 and TM6 - while the agonist stabilizes the active conformation. In some receptors, this is complemented by the formation of an “activating ionic lock” between TM5 and TM6 [5-8]. These dual contributions are considered thermodynamically essential for full activation [7].<br>\r\nAdding further complexity, GPCR activity is regulated by conformational microswitches and finely tuned intra-protein interaction networks. These dynamic rearrangements are difficult to capture and often elude direct correlation with functional outcomes. Moreover, allosteric ligands - which bind sites distinct from the orthosteric pocket - are being increasingly identified [9-12], along with small molecules capable of biased signaling, i.e., preferential activation of specific intracellular pathways [11-13, 16, 17]. These findings reveal a rich and underexplored conformational landscape that governs GPCR signaling. In addition, native membrane components—such as lipids and interacting proteins, including GPCR oligomers—are known to significantly modulate receptor function [11, 18-22].<br>\r\nTo disentangle these intricacies, computational modeling has become indispensable, offering atomistic insight into GPCR conformational dynamics and mechanistic understanding [1-2, 7, 11, 14, 16–21, 23]. Nevertheless, key questions remain - particularly regarding the structural basis of biased signaling, strategies for leveraging allosteric networks in pharmacology, and the modulatory role of the lipid environment. Addressing these gaps is crucial for both fundamental biology and the rational design of next-generation GPCR-targeting drugs with improved selectivity and safety profiles. <br>\r\nThese scientific challenges form the foundation of our upcoming workshop, which will focus on the latest experimental and computational approaches for studying the functional dynamics of GPCRs. Given the profound health, economic, and societal implications of modulating these receptors with precision, we aim to strengthen the interdisciplinary nature of the event by increasing the representation of experimental research and integrating cutting-edge artificial intelligence applications into the program.<br>\r\nBuilding upon the success of the 2022 and 2024 editions - which led to new collaborations and a landmark publication in <em>Nature Reviews Drug Discovery</em> [24] - our goal is to further enhance communication and collaboration between experimentalists and theoreticians. The workshop will serve as a reference point for young scientists and students, offering a platform to interact with leading international experts. We are confident that this initiative will foster insightful discussions and contribute meaningfully to advancing the field of GPCR pharmacology.<br>\r\n<br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://doi.org/10.1038/nrd.2017.229\" target=\"_blank\">[1] J. Smith, R. Lefkowitz, S. Rajagopal, Nat. Rev. Drug. Discov., <strong>17</strong>, 243-260 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41573-024-01083-3\" target=\"_blank\">[2] P. Conflitti, E. Lyman, M. Sansom, P. Hildebrand, H. Gutiérrez-de-Terán, P. Carloni, T. Ansell, S. Yuan, P. Barth, A. Robinson, C. Tate, D. Gloriam, S. Grzesiek, M. Eddy, S. Prosser, V. Limongelli, Nat. Rev. Drug. Discov., <strong>24</strong>, 251-275 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41589-024-01682-6\" target=\"_blank\">[3] L. Picard, A. Orazietti, D. Tran, A. Tucs, S. Hagimoto, Z. Qi, S. Huang, K. Tsuda, A. Kitao, A. Sljoka, R. Prosser, Nat. Chem. Biol., <strong>21</strong>, 71-79 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.drudis.2020.10.006\" target=\"_blank\">[4] B. Huang, C. St. Onge, H. Ma, Y. Zhang, Drug Discovery Today, <strong>26</strong>, 189-199 (2021)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41467-023-42082-z\" target=\"_blank\">[5] D. Di Marino, P. Conflitti, S. Motta, V. Limongelli, Nat. Commun., <strong>14</strong>, 6439 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.ceb.2018.10.007\" target=\"_blank\">[6] G. Milligan, R. Ward, S. Marsango, Current Opinion in Cell Biology, <strong>57</strong>, 40-47 (2019)</a><br>\r\n<a href=\"https://doi.org/10.7554/elife.73901\" target=\"_blank\">[7] S. Huang, O. Almurad, R. Pejana, Z. Morrison, A. Pandey, L. Picard, M. Nitz, A. Sljoka, R. Prosser, eLife, <strong>11</strong>, (2022)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-pharmtox-010919-023411\" target=\"_blank\">[8] A. Duncan, W. Song, M. Sansom, Annu. Rev. Pharmacol. Toxicol., <strong>60</strong>, 31-50 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41467-025-60003-0\" target=\"_blank\">[9] A. Morales-Pastor, T. Miljuš, M. Dieguez-Eceolaza, T. Stępniewski, V. Ledesma-Martin, F. Heydenreich, T. Flock, B. Plouffe, C. Le Gouill, J. Duchaine, D. Sykes, C. Nicholson, E. Koers, W. Guba, A. Rufer, U. Grether, M. Bouvier, D. Veprintsev, J. Selent, Nat. Commun., <strong>16</strong>, 5265 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41586-022-05588-y\" target=\"_blank\">[10] A. Faouzi, H. Wang, S. Zaidi, J. DiBerto, T. Che, Q. Qu, M. Robertson, M. Madasu, A. El Daibani, B. Varga, T. Zhang, C. Ruiz, S. Liu, J. Xu, K. Appourchaux, S. Slocum, S. Eans, M. Cameron, R. Al-Hasani, Y. Pan, B. Roth, J. McLaughlin, G. Skiniotis, V. Katritch, B. Kobilka, S. Majumdar, Nature, <strong>613</strong>, 767-774 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41467-022-31652-2\" target=\"_blank\">[11] M. Wall, E. Hill, R. Huckstepp, K. Barkan, G. Deganutti, M. Leuenberger, B. Preti, I. Winfield, S. Carvalho, A. Suchankova, H. Wei, D. Safitri, X. Huang, W. Imlach, C. La Mache, E. Dean, C. Hume, S. Hayward, J. Oliver, F. Zhao, D. Spanswick, C. Reynolds, M. Lochner, G. Ladds, B. Frenguelli, Nat. Commun., <strong>13</strong>, 4150 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41580-018-0049-3\" target=\"_blank\">[12] D. Wootten, A. Christopoulos, M. Marti-Solano, M. Babu, P. Sexton, Nat. Rev. Mol. Cell. Biol., <strong>19</strong>, 638-653 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41594-017-0011-7\" target=\"_blank\">[13] D. Hilger, M. Masureel, B. Kobilka, Nat. Struct. Mol. Biol., <strong>25</strong>, 4-12 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41467-025-57034-y\" target=\"_blank\">[14] D. Aranda-García, T. Stepniewski, M. Torrens-Fontanals, A. García-Recio, M. Lopez-Balastegui, B. Medel-Lacruz, A. Morales-Pastor, A. Peralta-García, M. Dieguez-Eceolaza, D. Sotillo-Nuñez, T. Ding, M. Drabek, C. Jacquemard, J. Jakowiecki, W. Jespers, M. Jiménez-Rosés, V. Jun-Yu-Lim, A. Nicoli, U. Orzel, A. Shahraki, J. Tiemann, V. Ledesma-Martin, F. Nerín-Fonz, S. Suárez-Dou, O. Canal, G. Pándy-Szekeres, J. Mao, D. Gloriam, E. Kellenberger, D. Latek, R. Guixà-González, H. Gutiérrez-de-Terán, I. Tikhonova, P. Hildebrand, M. Filizola, M. Babu, A. Di Pizio, S. Filipek, P. Kolb, A. Cordomi, T. Giorgino, M. Marti-Solano, J. Selent, Nat. Commun., <strong>16</strong>, 2020 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41586-018-0259-z\" target=\"_blank\">[15] D. Thal, A. Glukhova, P. Sexton, A. Christopoulos, Nature, <strong>559</strong>, 45-53 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.tips.2020.12.005\" target=\"_blank\">[16] L. Slosky, M. Caron, L. Barak, Trends in Pharmacological Sciences, <strong>42</strong>, 283-299 (2021)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.apsb.2023.07.020\" target=\"_blank\">[17] C. Zhu, X. Lan, Z. Wei, J. Yu, J. Zhang, Acta Pharmaceutica Sinica B, <strong>14</strong>, 67-86 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.chempr.2024.08.004\" target=\"_blank\">[18] V. D’Amore, P. Conflitti, L. Marinelli, V. Limongelli, Chem, <strong>10</strong>, 3678-3698 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41557-023-01238-6\" target=\"_blank\">[19] A. Mafi, S. Kim, W. Goddard, Nat. Chem., <strong>15</strong>, 1127-1137 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41594-024-01334-2\" target=\"_blank\">[20] H. Batebi, G. Pérez-Hernández, S. Rahman, B. Lan, A. Kamprad, M. Shi, D. Speck, J. Tiemann, R. Guixà-González, F. Reinhardt, P. Stadler, M. Papasergi-Scott, G. Skiniotis, P. Scheerer, B. Kobilka, J. Mathiesen, X. Liu, P. Hildebrand, Nat. Struct. Mol. Biol., <strong>31</strong>, 1692-1701 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.cell.2015.04.043\" target=\"_blank\">[21] A. Manglik, T. Kim, M. Masureel, C. Altenbach, Z. Yang, D. Hilger, M. Lerch, T. Kobilka, F. Thian, W. Hubbell, R. Prosser, B. Kobilka, Cell, <strong>161</strong>, 1101-1111 (2015)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.cell.2020.03.003\" target=\"_blank\">[22] M. Congreve, C. de Graaf, N. Swain, C. Tate, Cell, <strong>181</strong>, 81-91 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41573-025-01139-y\" target=\"_blank\">[23] J. Lorente, A. Sokolov, G. Ferguson, H. Schiöth, A. Hauser, D. Gloriam, Nat. Rev. Drug. Discov., <strong>24</strong>, 458-479 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1111/bph.16495\" target=\"_blank\">[24] M. Lopez‐Balastegui, T. Stepniewski, M. Kogut‐Günthel, A. Di Pizio, M. Rosenkilde, J. Mao, J. Selent, British. J. Pharmacology., <strong>182</strong>, 3211-3224 (2024)</a>\r\n</p><div class=\"active tab-pane\"> </div>", "image_description": "", "creation_date": "2026-01-26T16:00:31", "last_modification_date": "2026-01-26T16:45:08", "link_label": "G protein-coupled receptors functional dynamics revealed by experimental and computational structura", "link_url": "https://www.cecam.org/workshop-details/g-protein-coupled-receptors-functional-dynamics-revealed-by-experimental-and-computational-structural-data-1488", "canceled": "False", "cancel_reason": "", "place_and_room": "Aula Magna, USI Lugano", "url_place_and_room": "https://www.desk.usi.ch/en/lugano-campus-map-access-facilities", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Vittorio Limongelli</strong>, Università della Svizzera Italiana USI Lugano ; <strong>Scott Prosser</strong>, University of Toronto ; <strong>Stefano Raniolo</strong>, Università della Svizzera Italiana ; <strong>Jana Selent</strong>, Hospital Del Mar Medical Research Institute", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119453/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 70957, "title": "From Data to Dynamics: Machine Learning in Statistical Mechanics and Molecular Simulations", "slug": "from-data-to-dynamics-machine-learning-in-statis-2", "event_url": "https://memento.epfl.ch/event/from-data-to-dynamics-machine-learning-in-statis-2", "visual_url": "https://memento.epfl.ch/image/32346/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32346/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32346/max-size.jpg", "lang": "en", "start_date": "2026-10-14", "end_date": "2026-10-16", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/from-data-to-dynamics-machine-learning-in-statistical-mechanics-and-molecular-simulations-1487\">https://www.cecam.org/workshop-details/from-data-to-dynamics-machine-learning-in-statistical-mechanics-and-molecular-simulations-1487</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\nSince its introduction in the 1970s, molecular dynamics (MD) has become an indispensable computational microscope for studying complex biological systems at atomic resolution. It has enabled detailed investigations into protein folding, conformational dynamics, and ligand binding and unbinding. Over the past decade, increasing computational power has made microsecond-scale simulations routine, producing massive datasets that demand sophisticated analysis strategies [1]. Despite these advances, conventional MD simulations still face a fundamental limitation: many biologically relevant events occur over milliseconds to seconds—timescales largely inaccessible to standard MD.<br>\r\nTo bridge this gap, researchers increasingly turn to enhanced sampling techniques—such as metadynamics and umbrella sampling [2,3]—and coarse-grained (CG) modeling approaches [4]. These methods enable more comprehensive exploration of the system’s free energy landscape, yet their success critically depends on the selection of appropriate reaction coordinates or collective variables (CVs). CVs must capture the slowest, most functionally relevant motions to accurately reflect thermodynamic and kinetic behavior. However, identifying suitable CVs remains one of the field’s most challenging tasks, typically requiring domain expertise and iterative refinement [5, 6].<br>\r\nThis complexity has fueled growing interest in machine learning (ML) techniques, which are now transforming how MD simulations are analyzed, interpreted, and even conducted. ML methods have been applied to automate CV discovery, perform dimensionality reduction, build thermodynamic and kinetic models, and enhance sampling efficiency [7]. These models often employ artificial neural networks or graph neural networks to map high-dimensional molecular configurations—such as Cartesian coordinates or molecular descriptors—into low-dimensional representations suitable for analysis [8].<br>\r\nDepending on the structure and type of data, ML algorithms can be broadly categorized into supervised, unsupervised, and reinforcement learning paradigms [9]. Supervised learning uses labeled input-output pairs to predict properties such as molecular energies or binding affinities [10], while unsupervised learning enables the identification of latent features, such as CVs, directly from data [11].<br>\r\nA cornerstone of modern ML-driven simulation is the development of symmetry-aware molecular representations. The predictive power of ML models hinges on encoding physical symmetries—like rotation and translation—directly into the model. E(3)-equivariant neural networks have emerged as powerful tools for this purpose, significantly improving data efficiency and generalization in learning potential energy surfaces [12]. Ongoing research continues to explore the optimal balance between enforcing strict symmetry and retaining model flexibility.<br>\r\nMeanwhile, breakthroughs in structural prediction—most notably the advent of AlphaFold 3—have revolutionized how researchers obtain initial molecular configurations. AlphaFold now provides remarkably accurate models of not only proteins but also their complexes with nucleic acids, ions, and small-molecule ligands [13]. However, these are static snapshots. They cannot capture dynamic behaviors, allosteric transitions, or binding kinetics—areas where physics-based simulations remain indispensable. Initial benchmarks suggest that even state-of-the-art predictors still fall short in modeling protein dynamics and ranking ligand binding affinities, further emphasizing the role of MD [14].<br>\r\nTo address the dimensionality and sampling bottlenecks, unsupervised ML approaches such as time-lagged autoencoders have reframed CV identification as a data-driven task. More recently, generative models—including diffusion models and variational autoencoders—have emerged as a new frontier. These models can learn the full conformational landscape of biomolecules and enable enhanced sampling, in some cases eliminating the need for predefined CVs altogether [15].<br>\r\nOnce accurate structural models and CVs are established, ML can significantly improve the estimation of thermodynamic and kinetic properties. In drug discovery, for instance, predicting protein–ligand binding affinity remains a central challenge. ML potentials trained on quantum mechanical data can be combined with enhanced sampling to yield highly accurate free energy landscapes and binding kinetics—results previously unattainable due to computational limitations [16]. However, challenges in data quality, model interpretability, and transferability remain critical areas of ongoing investigation [17].<br>\r\nFinally, ML is driving a renaissance in CG modeling. Deep neural networks can now learn many-body CG potentials directly from all-atom simulations, capturing emergent properties and enhancing transferability [18]. These models open the door to longer, larger-scale simulations with greater physical accuracy.<br>\r\nIn this rapidly evolving context, it becomes imperative to critically assess both the promise and limitations of ML in biomolecular simulation. The excitement surrounding these developments must be tempered by careful validation and benchmarking. This workshop thus serves as a timely opportunity—especially for early-career researchers—to explore these cutting-edge methods, engage in constructive dialogue, and chart new directions in the application of machine learning to molecular dynamics and drug discovery.<br>\r\n <br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://doi.org/10.1103/physrevlett.98.146401\" target=\"_blank\">[1] J. Behler, M. Parrinello, Phys. Rev. Lett., <strong>98</strong>, 146401 (2007)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.sbi.2024.102972\" target=\"_blank\">[2] P. Sahrmann, G. Voth, Current Opinion in Structural Biology, <strong>90</strong>, 102972 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jcim.2c01127\" target=\"_blank\">[3] K. Kříž, L. Schmidt, A. Andersson, M. Walz, D. van der Spoel, J. Chem. Inf. Model., <strong>63</strong>, 412-431 (2023)</a><br>\r\n<a href=\"https://doi.org/10.3389/fmolb.2022.899805\" target=\"_blank\">[4] K. Ahmad, A. Rizzi, R. Capelli, D. Mandelli, W. Lyu, P. Carloni, Front. Mol. Biosci., <strong>9</strong>, (2022)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-physchem-083122-125941\" target=\"_blank\">[5] S. Mehdi, Z. Smith, L. Herron, Z. Zou, P. Tiwary, Annual Review of Physical Chemistry, <strong>75</strong>, 347-370 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1101/2025.04.07.647682\" target=\"_blank\">[6] H. Zheng, H. Lin, A. Alade, J. Chen, E. Monroy, M. Zhang, J. Wang, AlphaFold3 in Drug Discovery: A Comprehensive Assessment of Capabilities, Limitations, and Applications, 2025</a><br>\r\n<a href=\"https://doi.org/10.1038/s41586-024-07487-w\" target=\"_blank\">[7] J. Abramson, J. Adler, J. Dunger, R. Evans, T. Green, A. Pritzel, O. Ronneberger, L. Willmore, A. Ballard, J. Bambrick, S. Bodenstein, D. Evans, C. Hung, M. O’Neill, D. Reiman, K. Tunyasuvunakool, Z. Wu, A. Žemgulytė, E. Arvaniti, C. Beattie, O. Bertolli, A. Bridgland, A. Cherepanov, M. Congreve, A. Cowen-Rivers, A. Cowie, M. Figurnov, F. Fuchs, H. Gladman, R. Jain, Y. Khan, C. Low, K. Perlin, A. Potapenko, P. Savy, S. Singh, A. Stecula, A. Thillaisundaram, C. Tong, S. Yakneen, E. Zhong, M. Zielinski, A. Žídek, V. Bapst, P. Kohli, M. Jaderberg, D. Hassabis, J. Jumper, Nature, <strong>630</strong>, 493-500 (2024)</a><br>\r\n[8] Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer, Max Welling, NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems, Article No.: 166, Pages 1970 - 1981 (2020)<br>\r\n<a href=\"https://doi.org/10.1080/00268976.2020.1737742\" target=\"_blank\">[9] H. Sidky, W. Chen, A. Ferguson, Molecular Physics, <strong>118</strong>, (2020)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.sbi.2019.12.016\" target=\"_blank\">[10] Y. Wang, J. Lamim Ribeiro, P. Tiwary, Current Opinion in Structural Biology, <strong>61</strong>, 139-145 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41586-018-0337-2\" target=\"_blank\">[11] K. Butler, D. Davies, H. Cartwright, O. Isayev, A. Walsh, Nature, <strong>559</strong>, 547-555 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-physchem-042018-052331\" target=\"_blank\">[12] F. Noé, A. Tkatchenko, K. Müller, C. Clementi, Annu. Rev. Phys. Chem., <strong>71</strong>, 361-390 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1080/23746149.2021.2006080\" target=\"_blank\">[13] S. Kaptan, I. Vattulainen, Advances in Physics: X, <strong>7</strong>, (2022)</a><br>\r\n<a href=\"https://doi.org/10.1002/wcms.1455\" target=\"_blank\">[14] V. Limongelli, WIREs. Comput. Mol. Sci., <strong>10</strong>, (2020)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.chemrev.0c01195\" target=\"_blank\">[15] A. Glielmo, B. Husic, A. Rodriguez, C. Clementi, F. Noé, A. Laio, Chem. Rev., <strong>121</strong>, 9722-9758 (2021)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.sbi.2018.11.005\" target=\"_blank\">[16] A. Pak, G. Voth, Current Opinion in Structural Biology, <strong>52</strong>, 119-126 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1021/jacs.6b05602\" target=\"_blank\">[17] M. Lelimousin, V. Limongelli, M. Sansom, J. Am. Chem. Soc., <strong>138</strong>, 10611-10622 (2016)</a><br>\r\n<a href=\"https://doi.org/10.3390/e16010163\" target=\"_blank\">[18] C. Abrams, G. Bussi, Entropy, <strong>16</strong>, 163-199 (2013)</a>\r\n</p><div class=\"active tab-pane\"> </div>", "image_description": "", "creation_date": "2026-01-26T16:07:22", "last_modification_date": "2026-01-26T16:45:31", "link_label": "From Data to Dynamics: Machine Learning in Statistical Mechanics and Molecular Simulations", "link_url": "https://www.cecam.org/workshop-details/from-data-to-dynamics-machine-learning-in-statistical-mechanics-and-molecular-simulations-1487", "canceled": "False", "cancel_reason": "", "place_and_room": "Aula Magna, USI Lugano", "url_place_and_room": "https://www.desk.usi.ch/en/lugano-campus-map-access-facilities", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Daniele Angioletti, </strong>Università della Svizzera Italiana (USI) ; <strong>Vincenzo Maria D'Amore, </strong>University of Naples \"Federico II\" ; <strong>Marco De Vivo, </strong>Istituto Italiano di Tecnologia ; <strong>Francesco Saverio Di Leva, </strong>University of Naples Federico II ; <strong>Vittorio Limongelli, </strong>Università della Svizzera Italiana USI Lugano ; <strong>Gregory Voth, </strong>University of Chicago", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119454/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 71015, "title": "HFSP | 2027 Postdoctoral Fellowships", "slug": "hfsp-2027-postdoctoral-fellowships", "event_url": "https://memento.epfl.ch/event/hfsp-2027-postdoctoral-fellowships", "visual_url": "https://memento.epfl.ch/image/32401/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32401/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32401/max-size.jpg", "lang": "en", "start_date": "2026-05-05", "end_date": "2026-05-05", "start_time": null, "end_time": null, "description": "<strong>Aim: </strong> The HFSP fellowship program supports proposals for frontier, potentially transformative research in the <strong>life sciences</strong>. Applications for <strong>high-risk</strong> projects are particularly encouraged. The projects should be <strong>interdisciplinary</strong> in nature and should challenge existing paradigms by using novel approaches and techniques. Scientifically, they should address an important problem or a barrier to progress in the field.<br>\r\n<br>\r\nHFSP postdoctoral fellowships encourage early career scientists to broaden their research skills by moving into new areas of study while working in a new country.<br>\r\n<br>\r\nTwo different fellowships are available:<br>\r\n \r\n<ul>\r\n\t<li><strong>Long-Term Fellowships (LTF) </strong>are for applicants with a PhD on a biological topic who want to embark on a novel and frontier project focusing on the life sciences.</li>\r\n\t<li><strong>Cross-Disciplinary Fellowships (CDF)</strong> are for applicants who hold a doctoral degree from a non-biological discipline (e.g. physics, chemistry, mathematics, engineering or computer sciences) and who have not worked in the life sciences before.</li>\r\n</ul>\r\n<br>\r\n<strong>Funding</strong>: Living allowance according to host country (see <a href=\"https://www.hfsp.org/sites/default/files/Sciences/fellows/2027/HFSP%20Fellowships%202027%20-%20Application%20Guidelines.pdf\">Guidelines</a>), as well as a research and travel allowance.<br>\r\n<br>\r\n<strong>Duration</strong>: 36 months<br>\r\n<br>\r\n<strong>Eligibility:</strong> A summary of eligibility criteria is given below. For full details, please see the <a href=\"https://www.hfsp.org/sites/default/files/Sciences/fellows/2027/HFSP%20Fellowships%202027%20-%20Application%20Guidelines.pdf\">Guidelines</a>.<br>\r\n \r\n<ul>\r\n\t<li><strong>Anyone from any country and any nationality can apply for a fellowship</strong>. However, candidates <u>cannot</u> apply for a fellowship to work in the country of their nationality, regardless of whether they have obtained their PhD degree in this or another country. Further, A candidate who is not a national of one of the HFSPO members (see page 5 of the Guidelines) may apply to work only in a research institution in one of the member countries. A candidate who is a national of one of the member countries can apply to work in a research institution in any country that they are not a national of.</li>\r\n\t<li><strong>Applicants must propose to work in a country different from the one where they did their previous PhD work or first post-doctoral studies.</strong> For those institutions that are not classified as national, i.e. international or extraterritorial institutions such as EMBL, ICPT or ICGEB, the country in which the laboratory is located will be considered the host country. Candidates should not have spent more than 12 months in their proposed host institution at the activation date of the fellowship.</li>\r\n\t<li><strong>A research doctorate (PhD)</strong> or a doctoral-level degree comparable to a PhD with equivalent experience in basic research (e.g. a research based MD or medical PhD) must be conferred by the start of the fellowship, but is not required at the time of submission. If PhD has already been awarded, The degree must have been conferred in the 3 years prior to the submission deadline</li>\r\n\t<li><strong>Applicants must propose a research topic different to that of their PhD</strong> <strong>and previous postdoctoral work.</strong> The proposal must align with the mission and objectives of HFSP and the scientific scope of the HFSP Fellowship program.</li>\r\n\t<li><strong>Applicants must have at least one full-length original research publication, </strong>in English, for which the applicant is a lead author (e.g. applicant is either the single author, first author, or joint-first author)<strong>.</strong> By the submission deadline of the Letter of Intent, the manuscript should be either i) already published, (ii) accepted and in press, or (iii) accepted for publication in a peer-reviewed journal, or (iv) available online in a recognized open-access (OA) preprint repository AND submitted to a peer-reviewed journal.</li>\r\n</ul>\r\n<br>\r\n<strong>How to Apply</strong>: <strong>Applications</strong> will follow a two-step submission process via the online submission platform <a href=\"https://proposalcentral.com/\"><strong>ProposalCentral</strong></a>. First, applicants will be asked to initiate a Letter of Intent by 5 May 2026; full instructions for the LoI submission can be found <a href=\"https://www.hfsp.org/sites/default/files/Sciences/fellows/2027/HFSP%20Fellowships%202027%20-%20PC%20instructions%20for%20LOI%20applicants_0.pdf\">here</a>.<br>\r\n<br>\r\nIn a second step, successful applicants (~15-20%) will be invited to submit a Full Application. If invited, full proposals must be submitted by 24 September 2026. Please note that the signing official of the host institution must sign off on the full proposal submission on ProposalCentral; please leave plenty of time for this step. If EPFL will be your host institution, please contact the <a href=\"mailto:[email protected]\">Research Office</a> for any questions on the institutional signoff.<br>\r\n<br>\r\n<strong>Deadline, LOI initiation:</strong> 5 May 2026<br>\r\n<br>\r\n<strong>Further information</strong>\r\n\r\n<ul>\r\n\t<li>More information about the program is available <a href=\"https://www.hfsp.org/funding/hfsp-funding/postdoctoral-fellowships\">here</a></li>\r\n\t<li>The application portal can be found <a href=\"https://proposalcentral.com/\">here</a></li>\r\n\t<li>For any other questions, please contact the <a href=\"mailto:[email protected]\">Research Office</a></li>\r\n</ul>", "image_description": "", "creation_date": "2026-02-02T15:29:45", "last_modification_date": "2026-02-02T17:12:31", "link_label": "", "link_url": "", "canceled": "False", "cancel_reason": "", "place_and_room": "", "url_place_and_room": "", "url_online_room": "", "spoken_languages": [], "speaker": "", "organizer": "", "contact": "<a href=\"mailto:[email protected]?subject=HFSP%20Fellowhsips\">Research Office</a>", "is_internal": "False", "theme": "", "vulgarization": { "id": 1, "fr_label": "Tout public", "en_label": "General public" }, "registration": { "id": 3, "fr_label": "Entrée libre", "en_label": "Free" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119546/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/140/?format=api" ] }, { "id": 71019, "title": "Summer School in Image Analysis", "slug": "summer-school-in-image-analysis-2", "event_url": "https://memento.epfl.ch/event/summer-school-in-image-analysis-2", "visual_url": "https://memento.epfl.ch/image/32406/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32406/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32406/max-size.jpg", "lang": "en", "start_date": "2026-06-08", "end_date": "2026-06-12", "start_time": null, "end_time": null, "description": "<p>A hands-on introduction to the key concepts in image analysis for your everyday research! <br>\r\n<br>\r\nOpen to PhD students from all doctoral programs at EPFL, Swiss academic institutions and ETH domain.<br>\r\n<br>\r\n<strong>June 8 to 12 2026 </strong><br>\r\n<strong>Palace de Caux, Montreux</strong><br>\r\nPre-summer school Workshop will take place on June 4, 2026 (afternoon)<br>\r\n<br>\r\n<strong><a href=\"https://imaging.epfl.ch/summer-school\">More info</a></strong><br>\r\n<strong><a href=\"https://docs.google.com/forms/d/e/1FAIpQLSdtHQLlAolAnXJHI5BKbgJXUJA5F6seAPbIOT7W1zKvkZ90wQ/viewform?usp=header\">Application</a></strong> Deadline: March 1, 2026<br>\r\n<br>\r\nAre you a PhD student at EPFL, at another swiss academic institution or within the ETH domaine who regularly faces questions regarding the analysis of your images ? Do you want to learn more about practical concepts and tools to help you in this endeavor? Then our summer school in image analysis is for you! Throughout the week, a series of lectures will provide you with the essential concepts in image analysis – from the nature of digital images through the physics of image acquisition to the basics of deep learning, and more. 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