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{ "count": 238, "next": "https://memento.epfl.ch/api/v1/events/?format=api&limit=10&offset=60&ordering=event__category", "previous": "https://memento.epfl.ch/api/v1/events/?format=api&limit=10&offset=40&ordering=event__category", "results": [ { "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": 70974, "title": "Brown Bag Seminar in Finance", "slug": "brown-bag-seminar-in-finance-13", "event_url": "https://memento.epfl.ch/event/brown-bag-seminar-in-finance-13", "visual_url": "https://memento.epfl.ch/image/32361/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32361/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32361/max-size.jpg", "lang": "en", "start_date": "2026-06-16", "end_date": "2026-06-16", "start_time": "12:15:00", "end_time": "13:15:00", "description": "", "image_description": "", "creation_date": "2026-01-27T10:56:12", "last_modification_date": "2026-01-27T10:56:12", "link_label": "", "link_url": "", "canceled": "False", "cancel_reason": "", "place_and_room": "UNIL, Extranef, room 126", "url_place_and_room": "", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "<a href=\"https://profiles.imperial.ac.uk/yang.liu19\">Mingyang Liu - PhD student, Imperial College London</a>", "organizer": "", "contact": "[email protected]\r\n\r\n ", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 2, "fr_label": "Sur invitation", "en_label": "Invitation required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119483/", "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/3/?format=api", "https://memento.epfl.ch/api/v1/mementos/116/?format=api", "https://memento.epfl.ch/api/v1/mementos/39/?format=api", "https://memento.epfl.ch/api/v1/mementos/105/?format=api" ] }, { "id": 70990, "title": "\"Mapping the Human Body One Cell at a Time.\"", "slug": "mapping-the-human-body-one-cell-at-a-time", "event_url": "https://memento.epfl.ch/event/mapping-the-human-body-one-cell-at-a-time", "visual_url": "https://memento.epfl.ch/image/32376/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32376/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32376/max-size.jpg", "lang": "en", "start_date": "2026-04-30", "end_date": "2026-04-30", "start_time": "12:15:00", "end_time": "13:15:00", "description": "<strong>A Lola and John Grace Distinguished Lecture in Cancer Research</strong>\r\n<div class=\"elementor-element elementor-element-98cefd8 elementor-widget elementor-widget-heading\">\r\n<div class=\"elementor-widget-container\"><br>\r\nSarah Teichmann completed her PhD at the MRC Laboratory of Molecular Biology in Cambridge, UK, and was a Beit Memorial Fellow at University College London. She established her research group at the MRC Laboratory of Molecular Biology in 2001, where her main discoveries included the finding that protein assembly pathways are stereotypical and conserved. In 2013, she transitioned to the Wellcome Genome Campus, where she became the first and, to date, the only faculty member appointed across both the EMBL-European Bioinformatics Institute and the Wellcome Sanger Institute. In 2016, she was appointed as the Head of the Cellular Genetics programme at the Wellcome Sanger Institute and co-founded the Human Cell Atlas initiative. From April 2024, she was appointed chair in Stem Cell Medicine at the University of Cambridge, within the Department of Medicine and the Cambridge Stem Cell Institute. Additionally, Sarah dedicates part of her time to GlaxoSmithKline and to EnsoCell Therapeutics, the startup company she co-founded. The Teichmann lab focuses on developing and applying cell atlas technologies to understand human tissue architecture, particularly examining how cellular diversity is generated in the immune system and during development.</div>\r\n</div>\r\n\r\n<div class=\"elementor-element elementor-element-e552a16 elementor-widget elementor-widget-text-editor\">\r\n<div class=\"elementor-widget-container\"> </div>\r\n</div>", "image_description": "", "creation_date": "2026-01-28T14:35:28", "last_modification_date": "2026-02-13T13:56:17", "link_label": "", "link_url": "", "canceled": "False", "cancel_reason": "", "place_and_room": "SV 1717", "url_place_and_room": "https://plan.epfl.ch/?room==SV%201717", "url_online_room": "https://epfl.zoom.us/j/97039045251?pwd=WDFjUGV3Y1FxeG9rTUIrOGpiUXhLQT09", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "<strong>Sarah Teichmann FMedSci FRS</strong> (Group Leader), Cambridge Stem Cell Institute & Department of Medicine, Jeffrey Cheah Biomedical Centre, University of Cambridge", "organizer": "Prof. Charlotte Bunne", "contact": "Lisa Smith, ISREC Administrative Assistant", "is_internal": "True", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 3, "fr_label": "Entrée libre", "en_label": "Free" }, "keywords": "Grace Lecture, cancer", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119505/", "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/26/?format=api", "https://memento.epfl.ch/api/v1/mementos/9/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api" ] }, { "id": 71003, "title": "Imaging Lunch: A Practical Introduction to Machine Learning for Vision Applications", "slug": "imaging-lunch-a-practical-introduction-to-machine", "event_url": "https://memento.epfl.ch/event/imaging-lunch-a-practical-introduction-to-machine", "visual_url": "https://memento.epfl.ch/image/32391/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32391/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32391/max-size.jpg", "lang": "en", "start_date": "2026-04-29", "end_date": "2026-04-29", "start_time": "11:00:00", "end_time": "13:00:00", "description": "<strong><a href=\"https://docs.google.com/forms/d/e/1FAIpQLSc1NukuuzIzzMnbDZkfudvj1M7wDJY6JeWQKoQrcl1ZIVFcUw/viewform?usp=header\">Registration</a><br>\r\n<br>\r\nAbstract</strong><br>\r\nThis workshop is designed for EPFL researchers interested in building effective, automated computer vision systems for scientific applications. During the workshop, we will review and explain the main steps of a machine learning-based computer vision project, from problem definition to image acquisition, annotation, training, validation, and inference. We will illustrate these steps in a practical way, by building a real computer vision application together until we can run it live on a camera. Along the way, we will discuss important concepts (data drift, overfitting, data augmentation) and observe how they manifest themselves in practice. We will also share tips and tricks to help you tackle your own computer vision challenges.<br>\r\n<br>\r\n<strong>Prerequisites:</strong><br>\r\nBasic familiarity with Python, image analysis, and machine learning concepts is desirable, but not strictly necessary.<br>\r\n<br>\r\n<strong>Level:</strong> <br>\r\nBeginner / Intermediate<br>\r\n ", "image_description": "", "creation_date": "2026-01-30T11:40:06", "last_modification_date": "2026-01-30T11:40:06", "link_label": "Registration", "link_url": "https://docs.google.com/forms/d/e/1FAIpQLSc1NukuuzIzzMnbDZkfudvj1M7wDJY6JeWQKoQrcl1ZIVFcUw/viewform?usp=header", "canceled": "False", "cancel_reason": "", "place_and_room": "CE 1 711.2", "url_place_and_room": "https://plan.epfl.ch/?room==CE%201%20711.2", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "<a href=\"https://people.epfl.ch/mallory.wittwer?lang=en\">Mallory Witwer</a>", "organizer": "<a href=\"https://imaging.epfl.ch/\">Center for Imaging</a>", "contact": "<a href=\"https://people.epfl.ch/cecilia.carron?lang=fr\">Cecilia Carron</a>", "is_internal": "False", "theme": "", "vulgarization": { "id": 1, "fr_label": "Tout public", "en_label": "General public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119528/", "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/4/?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/9/?format=api", "https://memento.epfl.ch/api/v1/mementos/284/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 71009, "title": "BMI Seminar // Sung Han: Revealing the role of neuropeptides in neural circuit function and behavior", "slug": "bmi-seminar-sung-han-revealing-the-role-of-neurope", "event_url": "https://memento.epfl.ch/event/bmi-seminar-sung-han-revealing-the-role-of-neurope", "visual_url": "https://memento.epfl.ch/image/32395/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32395/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32395/max-size.jpg", "lang": "en", "start_date": "2026-04-13", "end_date": "2026-04-13", "start_time": "14:00:00", "end_time": "15:00:00", "description": "<p>Neuropeptides play a critical role in modulating neural circuits and influencing behavior, yet their precise mechanisms of action remain largely unexplored. In this talk, I will present our latest findings on the role of neuropeptides in neural circuit function, uncovered through the use of novel genetically encoded sensors and silencers designed specifically for peptidergic transmission. By applying these innovative tools, we have identified key pathways and mechanisms by which neuropeptides regulate neuronal activity and orchestrate complex behavioral responses. This presentation will highlight the biological insights gained from these studies, offering a deeper understanding of how neuropeptide signaling shapes brain function and behavior.<br>\r\n </p>", "image_description": "", "creation_date": "2026-01-30T16:01:35", "last_modification_date": "2026-02-10T16:25:35", "link_label": "Web Page", "link_url": "https://www.salk.edu/scientist/sung-han/", "canceled": "False", "cancel_reason": "", "place_and_room": "SV 1717", "url_place_and_room": "https://plan.epfl.ch/?room==SV%201717", "url_online_room": "https://epfl.zoom.us/j/64813563657", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "Sung Han, Salk Institute for Biological Studies", "organizer": "<strong>BMI LSYM</strong> <strong>Host: Ralf Schneggenburger</strong>", "contact": "[email protected]", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 3, "fr_label": "Entrée libre", "en_label": "Free" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119537/", "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/19/?format=api", "https://memento.epfl.ch/api/v1/mementos/9/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?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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In mythology and storytelling, rivers and fountains bring life, healing, and uncontrollable power. Since modernism, we have pursued watertight environments, sealing interiors while buried streams and pipes persist beneath our streets and walls. When a leak appears, this fiction collapses. Forgotten infrastructures surface, and suppressed ecologies emerge—moss, humidity, micro-habitats. Leaks become reminders of water’s formative power, suggesting possible futures for architecture and urban space.</em><br>\r\n<br>\r\nTruwant + Rodet + is a Basel-based architecture practice founded in 2017. The diverse background of its partners informs a curious and interdisciplinary practice connecting architecture with landscape, furniture design, research and teaching. Their work focuses on the transformation of existing structures and on the relationship between building, context and landscape, understanding architecture as part of a broader territorial framework.<br>\r\n<br>\r\nIn 2017, they received the Swiss Art Award for “A Pavilion.” In 2018, they co-founded the Basel cultural platform dasVerein with Melissa Freudiger. In 2020, together with ASBR, they won the competition for the renovation of the Centre Culturel Suisse in Paris, completed in 2026. In 2023, they won the competition for the adaptive reuse of the former Nauentor postal center in Basel with Bruther and Jan Kinsbergen, as well as the competition for affordable housing in Basel with Clauss Kahl Merz. Since 2023, they have been guest teachers at EPFL Lausanne.<br>\r\n<br>\r\n<br>\r\nThis lecture is part of the School Lecture Series<br>\r\n<br>\r\n<strong>COMMUNITY VOL.2</strong><br>\r\nSeven exemplary projects and case studies<br>\r\n<br>\r\nCommunity is an ambivalent concept. It involves both gathering through shared customs and exclusion. Some claim that inclusive communities do not exist. Recent history shows people more often unite through exclusion than inclusion. However, communities are not sealed. Philosopher Roberto Esposito explains that community ‘is not a property or territory to defend but a void, a debt, and a gift to others’, reminding us of our otherness.<br>\r\n<br>\r\nThis lecture series explores the topic of community through architecture. How does architecture explore, define, or enable communities? Can architects collaborate directly with communities, bypassing institutional entities? How can design convey a collective experience? 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