You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/toward-intelligent-behavior-in-macroscopic-active-matter-1481.
\r\n
\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 CECAM Event Manager if you have any question.
\r\n
\r\nDescription
\r\n
\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.
\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.
\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.
\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.
\r\n
\r\nReferences
\r\n
\r\n[1] F. Gu, B. Guiselin, N. Bain, I. Zuriguel, D. Bartolo, Nature, 638, 112-119 (2025)
\r\n[2] A. Rafsanjani, K. Bertoldi, A. Studart, Sci. Robot., 4, (2019)
\r\n[3] J. Tirado, A. Parvaresh, B. Seyidoğlu, D. Bedford, J. Jørgensen, A. Rafsanjani, Cyborg. Bionic. Syst., 6, (2025)
\r\n[4] J. O’Byrne, Y. Kafri, J. Tailleur, F. van Wijland, Nat. Rev. Phys., 4, 167-183 (2022)
\r\n[5] P. Baconnier, D. Shohat, C. López, C. Coulais, V. Démery, G. Düring, O. Dauchot, Nat. Phys., 18, 1234-1239 (2022)
\r\n[6] A. Cavagna, L. Di Carlo, I. Giardina, T. Grigera, S. Melillo, L. Parisi, G. Pisegna, M. Scandolo, Nat. Phys., 19, 1043-1049 (2023)
\r\n[7] M. Bischof, E. Del Giudice, Molecular Biology International, 2013, 1-19 (2013)
\r\n[8] A. Deutsch, P. Friedl, L. Preziosi, G. Theraulaz, Phil. Trans. R. Soc. B, 375, 20190377 (2020)
\r\n[9] N. Kumar, H. Soni, S. Ramaswamy, A. Sood, Nat. Commun., 5, 4688 (2014)
\r\n[10] M. Moussaid, S. Garnier, G. Theraulaz, D. Helbing, Topics in Cognitive Science, 1, 469-497 (2009)
\r\n[11] R. Bebon, J. Robinson, T. Speck, Phys. Rev. X, 15, 021050 (2025)
\r\n[12] M. Ben Zion, J. Fersula, N. Bredeche, O. Dauchot, Sci. Robot., 8, (2023)
\r\n[13] J. Fersula, N. Bredeche, O. Dauchot, Phys. Rev. E, 110, 014606 (2024)
\r\n[14] L. Caprini, A. Ldov, R. Gupta, H. Ellenberg, R. Wittmann, H. Löwen, C. Scholz, Commun. Phys., 7, 52 (2024)
\r\n[15] T. Lengronne, D. Mlynski, S. Patalano, R. James, L. Keller, S. Sumner, Proc. R. Soc. B., 288, rspb.2021.0275 (2021)
\r\n[16] T. Vicsek, A. Czirók, E. Ben-Jacob, I. Cohen, O. Shochet, Phys. Rev. Lett., 75, 1226-1229 (1995)
\r\n[17] L. Ning, H. Zhu, J. Yang, Q. Zhang, P. Liu, R. Ni, N. Zheng, NSO., 3, 20240005 (2024)
\r\n[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, 37, 333501 (2025)
\r\n[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, 32, 193001 (2020)
\r\n[20] G. Popkin, Nature, 529, 16-18 (2016)
You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/multi-scale-and-multi-purpose-simulations-of-dna-the-importance-of-data-1484.
\r\n
\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 CECAM Event Manager if you have any question.
\r\n
\r\nDescription
\r\n
\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).
\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).
\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.
\r\n
\r\nReferences
\r\n
\r\n[1] J. Aranda, M. Terrazas, H. Gómez, N. Villegas, M. Orozco, Nat. Catal., 2, 544-552 (2019)
\r\n[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, 47, 9511-9523 (2019)
\r\n[3] R. Collepardo-Guevara, G. Portella, M. Vendruscolo, D. Frenkel, T. Schlick, M. Orozco, J. Am. Chem. Soc., 137, 10205-10215 (2015)
\r\n[4] P. Dans, I. Ivani, A. Hospital, G. Portella, C. González, M. Orozco, Nucleic. Acids. Res., gkw1355 (2017)
\r\n[5] J. de Pablo, Annu. Rev. Phys. Chem., 62, 555-574 (2011)
\r\n[6] D. Farré-Gil, J. Arcon, C. Laughton, M. Orozco, Nucleic Acids Research, 52, 6791-6801 (2024)
\r\n[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, 53, (2025)
\r\n[8] J. Joseph, A. Reinhardt, A. Aguirre, P. Chew, K. Russell, J. Espinosa, A. Garaizar, R. Collepardo-Guevara, Nat. Comput. Sci., 1, 732-743 (2021)
\r\n[9] Z. Li, T. Schlick, Nucleic Acids Research, 52, 583-599 (2023)
\r\n[10] S. Liu, C. Wang, B. Zhang, Biochemistry, 64, 1750-1761 (2025)
\r\n[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., 29, 1011-1023 (2022)
\r\n[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)
\r\n[13] E. Schultz, J. Kaplan, Y. Wu, S. Kyhl, R. Willett, J. de Pablo, WIREs. Comput. Mol. Sci., 15, (2025)
You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/spectrodynamics-2026-connecting-computational-spectroscopic-methods-across-the-electromagnetic-spectrum-1489.
\r\n
\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 CECAM Event Manager if you have any question.
\r\n
\r\nDescription
\r\n
\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].
\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.
\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.
\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].
\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].
\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.
\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).
\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.
\r\n
\r\nReferences
\r\n
\r\n[1] P. Hamm, The Journal of Chemical Physics, 141, (2014)
\r\n[2] M. Svendsen, K. Thygesen, A. Rubio, J. Flick, J. Chem. Theory Comput., 20, 926-936 (2024)
\r\n[3] F. Bonafé, E. Albar, S. Ohlmann, V. Kosheleva, C. Bustamante, F. Troisi, A. Rubio, H. Appel, Phys. Rev. B, 111, 085114 (2025)
\r\n[4] M. Ruggenthaler, J. Flick, C. Pellegrini, H. Appel, I. Tokatly, A. Rubio, Phys. Rev. A, 90, 012508 (2014)
\r\n[5] J. Flick, D. Welakuh, M. Ruggenthaler, H. Appel, A. Rubio, ACS Photonics, 6, 2757-2778 (2019)
\r\n[6] A. Sakko, A. Rubio, M. Hakala, K. Hämäläinen, The Journal of Chemical Physics, 133, (2010)
\r\n[7] D. Varsano, L. Espinosa-Leal, X. Andrade, M. Marques, R. di Felice, A. Rubio, Phys. Chem. Chem. Phys., 11, 4481 (2009)
\r\n[8] K. Yabana, G. Bertsch, Phys. Rev. B, 54, 4484-4487 (1996)
\r\n[9] Y. Litman, J. Behler, M. Rossi, Faraday Discuss., 221, 526-546 (2020)
\r\n[10] S. Althorpe, Annual Review of Physical Chemistry, 75, 397-420 (2024)
\r\n[11] M. Ceriotti, W. Fang, P. Kusalik, R. McKenzie, A. Michaelides, M. Morales, T. Markland, Chem. Rev., 116, 7529-7550 (2016)
\r\n[12] M. Gastegger, J. Behler, P. Marquetand, Chem. Sci., 8, 6924-6935 (2017)
\r\n[13] R. Han, R. Ketkaew, S. Luber, J. Phys. Chem. A, 126, 801-812 (2022)
\r\n[14] K. Inoue, Y. Litman, D. Wilkins, Y. Nagata, M. Okuno, J. Phys. Chem. Lett., 14, 3063-3068 (2023)
\r\n[15] T. Morawietz, O. Marsalek, S. Pattenaude, L. Streacker, D. Ben-Amotz, T. Markland, J. Phys. Chem. Lett., 9, 851-857 (2018)
\r\n[16] Y. Litman, J. Lan, Y. Nagata, D. Wilkins, J. Phys. Chem. Lett., 14, 8175-8182 (2023)
\r\n[17] D. Nicoletti, A. Cavalleri, Adv. Opt. Photon., 8, 401 (2016)
\r\n[18] T. Ohto, K. Usui, T. Hasegawa, M. Bonn, Y. Nagata, The Journal of Chemical Physics, 143, (2015)
\r\n[19] M. Sulpizi, M. Salanne, M. Sprik, M. Gaigeot, J. Phys. Chem. Lett., 4, 83-87 (2012)
\r\n[20] O. Marsalek, T. Markland, J. Phys. Chem. Lett., 8, 1545-1551 (2017)
\r\n[21] C. Zhang, D. Donadio, F. Gygi, G. Galli, J. Chem. Theory Comput., 7, 1443-1449 (2011)
\r\n[22] D. Keefer, S. Cavaletto, J. Rouxel, M. Garavelli, H. Yong, S. Mukamel, Annu. Rev. Phys. Chem., 74, 73-97 (2023)
\r\n[23] S. Cavaletto, Y. Nam, J. Rouxel, D. Keefer, H. Yong, S. Mukamel, J. Chem. Theory Comput., 19, 2327-2339 (2023)
\r\n[24] D. Keefer, T. Schnappinger, R. de Vivie-Riedle, S. Mukamel, Proc. Natl. Acad. Sci. U.S.A., 117, 24069-24075 (2020)
\r\n[25] M. Kowalewski, B. Fingerhut, K. Dorfman, K. Bennett, S. Mukamel, Chem. Rev., 117, 12165-12226 (2017)
\r\n[26] Shaul Mukamel, Principles of nonlinear optical spectroscopy, Oxford University Press, New York 1995
\r\n[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, 582, 520-524 (2020)
\r\n[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., 114, 255501 (2015)
\r\n[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, 467, 440-443 (2010)
\r\n[30] D. Brey, R. Binder, R. Martinazzo, I. Burghardt, Faraday Discuss., 237, 148-167 (2022)
\r\n[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., 120, 7152-7218 (2020)
You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/bridging-biomolecular-simulations-and-experiments-across-time-and-length-scales-from-single-molecules-to-entire-organelles-1493.
\r\n
\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 CECAM Event Manager if you have any question.
\r\n
\r\nDescription
\r\n
\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.
\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.
\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.
\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.
Open-source hardware can play a unique role for the semiconductor industry in the age of sustainable AI. It can enable design reuse, foster collaboration, and support workforce development. ESP (Embedded Scalable Platforms) is an open-source research platform for system-on-chip (SoC) design that combines a modular architecture with an agile design methodology. The ESP architecture simplifies the design and prototyping of heterogeneous chips with multiple RISC-V processor cores and dozens of loosely coupled accelerators, all interconnected by a scalable network-on-chip. The ESP methodology promotes system-level design while accommodating different specification languages and design flows.
\r\n
\r\nESP's capabilities have enabled a small team, primarily composed of graduate students, to realize two SoCs of increasing complexity, each within a few months. Conceived as a heterogeneous system integration platform and refined through years of teaching at Columbia University, ESP is well suited to advance collaborative engineering across the open-source hardware community.
\r\n
\r\nThis talk will be followed by a standing lunch in BC420 from 12:15 to 13:30.
Thesis Directors: Prof. J. Baudry, Dr F. Jarrige
\r\nArchitecture and Sciences of the City doctoral program
\r\nThesis Nr. 11656
\r\n
\r\nTo take part in the public defense, please contact directly the speaker
Thesis Director: Prof. G. De Micheli,
\r\nComputer and Communication Sciences doctoral program
\r\nThesis Nr. 11412
\r\n
\r\nTo take part in the public defense, please contact directly the speaker