Event List
retrieve:
Return the details about the given Event id.
list:
List all Event objects.
GET /api/v1/events/?format=api&offset=80&ordering=slug
{ "count": 214, "next": "https://memento.epfl.ch/api/v1/events/?format=api&limit=10&offset=90&ordering=slug", "previous": "https://memento.epfl.ch/api/v1/events/?format=api&limit=10&offset=70&ordering=slug", "results": [ { "id": 61304, "title": "FondAction contre le Cancer | Grants for applied cancer research", "slug": "fondaction-contre-le-cancer-grants-for-applied-c-7", "event_url": "https://memento.epfl.ch/event/fondaction-contre-le-cancer-grants-for-applied-c-7", "visual_url": "https://memento.epfl.ch/image/23395/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/23395/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/23395/max-size.jpg", "lang": "en", "start_date": "2026-09-15", "end_date": "2026-09-15", "start_time": null, "end_time": null, "description": "<p>Established in Lausanne in 1999, <strong>FondAction contre le Cancer</strong> supports <strong>applied cancer research</strong> for the <strong>benefit of patients</strong>, particularly in <strong>Switzerland</strong>. The foundation offers 2 funding instruments:<br>\r\n<br>\r\n<strong>GRANTS </strong>\r\n</p><ul>\r\n\t<li><strong>What:</strong> For collaborative applied research projects related to cancer. The research topic can be preclinical, translational, clinical or of public health. A priority will be given to projects involving the development of innovative therapies for patients. Preclinical research should not be basic research but should be as close<br>\r\n\tto the clinic as possible.</li>\r\n\t<li><strong>Who can apply:</strong> team of applicants. The main applicant (MD, PhD, nurse or else) must be <strong>Swiss or be employed by a Swiss institution</strong> and should demonstrate the ability to lead and finalize an innovative research project.</li>\r\n\t<li><strong>Funding</strong>: no upper or lower limit. The grant does NOT cover the main applicant’s salary. You must have and mention other sources of funding.</li>\r\n\t<li><strong>Duration:</strong> max 36 months.</li>\r\n\t<li>More details <a href=\"https://www.fondaction.ch/wp-content/uploads/2025/04/Grant-Request-conditions-and-rules_2025.pdf\">here</a></li>\r\n</ul>\r\n<br>\r\n<strong>YOUNG INVESTIGATOR GRANTS</strong>\r\n\r\n<ul>\r\n\t<li><strong>What:</strong> Support to young researchers in the field of oncology <strong>in</strong> <strong>Switzerland</strong>, to help them at the beginning of their academic career. The research topic can be pre-clinical or clinical or of public health.</li>\r\n\t<li><strong>Who can apply:</strong> the young investigator must be an MD, a PhD in biology or health care, be <strong>Swiss or be employed by a Swiss institution</strong>, < 40 year old, have the support of his/her supervisor and the opportunity to build a career at the institution of employment.</li>\r\n\t<li><strong>Funding: </strong>CHF 100’000. The grant does NOT cover the main applicant’s salary.</li>\r\n\t<li><strong>Duration:</strong> 24 months.</li>\r\n\t<li>More details <a href=\"https://www.fondaction.ch/wp-content/uploads/2025/04/YIA-conditions-and-rules_2025.pdf\">here</a></li>\r\n</ul>\r\n<strong>Deadline: 15 September 2026</strong><br>\r\n<br>\r\nProposals are evaluated by the foundation’s <a href=\"https://www.fondaction.ch/fondation/conseil-de-fondation/\">scientific committee</a> and external experts.<br>\r\n<br>\r\n<strong>For more information</strong>, please have a look at the <a href=\"https://www.fondaction.ch/\">call webpage + supported projects</a><u>.</u><br>\r\n ", "image_description": "", "creation_date": "2022-12-16T18:09:01", "last_modification_date": "2026-05-18T15:30:33", "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=%5B%20FondAction%20contre%20le%20Cancer%20%7C%20Grants%20for%20applied%20cancer%20research%20%5D\">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/105284/", "category": { "id": 16, "code": "PROP", "fr_label": "Appel à proposition", "en_label": "Call for proposal", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/140/?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": 71751, "title": "From Exploitation to Mitigation: Analyzing Memory Corruption Primitives to Harden the Linux Kernel", "slug": "from-exploitation-to-mitigation-analyzing-memory-c", "event_url": "https://memento.epfl.ch/event/from-exploitation-to-mitigation-analyzing-memory-c", "visual_url": "https://memento.epfl.ch/image/33078/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/33078/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/33078/max-size.jpg", "lang": "en", "start_date": "2026-06-23", "end_date": "2026-06-23", "start_time": "10:00:00", "end_time": "12:00:00", "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. George Candea<br>\r\nThesis advisor: Prof. Mathias Payer<br>\r\nThesis co-advisor: Dr. Anil Kurmus<br>\r\nCo-examiner: Prof. Sanidhya Kashyap<br>\r\n<br>\r\n<u>Abstract</u><br>\r\ncoming soon<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\ncoming soon", "image_description": "", "creation_date": "2026-04-29T09:44:15", "last_modification_date": "2026-04-29T09:44:15", "link_label": "", "link_url": "", "canceled": "False", "cancel_reason": "", "place_and_room": "BC 133", "url_place_and_room": "https://plan.epfl.ch/?room==BC%20133", "url_online_room": "", "spoken_languages": [], "speaker": "Claudio Migliorelli", "organizer": "", "contact": "[email protected]", "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": "EDIC candidacy exam", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/120583/", "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/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api", "https://memento.epfl.ch/api/v1/mementos/48/?format=api" ] }, { "id": 71934, "title": "Future of Health Grant", "slug": "future-of-health-grant-3", "event_url": "https://memento.epfl.ch/event/future-of-health-grant-3", "visual_url": "https://memento.epfl.ch/image/33245/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/33245/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/33245/max-size.jpg", "lang": "en", "start_date": "2026-07-31", "end_date": "2026-07-31", "start_time": "11:00:00", "end_time": "12:00:00", "description": "<p>The <strong>Future of Health Grant</strong> is a support program dedicated to accelerating early‑stage digital health startups developing solutions relevant to the Swiss healthcare system.<br>\r\nCreated by CSS and the EPFL Innovation Park, it combines equity‑free funding, expert guidance and access to healthcare stakeholders to help founders progress from prototype to real‑world validation.<br>\r\n<br>\r\n<strong>For whom</strong>\r\n</p><ul>\r\n\t<li>For early‑stage digital health startups developing solutions that can be applied within the Swiss healthcare system.</li>\r\n\t<li>Eligible teams may be based in Switzerland or abroad, provided their innovation is relevant to Swiss clinical, operational or preventive care contexts.</li>\r\n\t<li>The program targets founders at the prototype, MVP or early validation stage.</li>\r\n</ul>\r\n<strong>What you gain</strong>\r\n\r\n<ul>\r\n\t<li><strong>equity‑free financial support</strong> at three possible levels (CHF 10k, 30k or 50k), depending on project maturity and needs</li>\r\n\t<li>teams benefit from <strong>structured coaching</strong>, <strong>industry mentoring</strong></li>\r\n\t<li>access to <strong>paid specialists</strong> through the “CxO as a Service” model</li>\r\n\t<li>The program also facilitates <strong>real‑world validation</strong> opportunities with healthcare professionals, hospitals, insurers and ecosystem partners in Switzerland</li>\r\n</ul>\r\n<strong>How it works</strong><br>\r\n<br>\r\nThe program operates fully virtually and follows a milestone‑based approach. Selected startups receive regular guidance from experts in digital health, business development and regulatory pathways. Each team works toward defined objectives, with progress reviewed at set intervals. The program supports founders in strengthening their product, business model and clinical relevance.<br>\r\n<strong>Program context</strong><br>\r\n<br>\r\nLaunched in 2022, the Future of Health Grant is an initiative of <strong>CSS</strong> and the <strong>EPFL Innovation Park</strong>. Its mission is to accelerate the adoption of impactful digital health solutions by bridging early‑stage innovation with the needs of real healthcare systems.", "image_description": "", "creation_date": "2026-05-22T13:39:38", "last_modification_date": "2026-05-22T13:39:44", "link_label": "Webpage", "link_url": "https://future-of-health.org/", "canceled": "False", "cancel_reason": "", "place_and_room": "", "url_place_and_room": "", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<a href=\"https://future-of-health.org/\">Future of Health Grant</a>, a programm powered by Fondation EPFL Innovation Park", "contact": "Mathilde Durvy <a href=\"mailto:[email protected]\">[email protected]</a>", "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/120842/", "category": { "id": 17, "code": "DEADLINE", "fr_label": "Echéance", "en_label": "Deadline", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/422/?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": 71930, "title": "Generative models for black-box optimization of complex objectives", "slug": "generative-models-for-black-box-optimization-of-co", "event_url": "https://memento.epfl.ch/event/generative-models-for-black-box-optimization-of-co", "visual_url": null, "visual_large_url": null, "visual_maxsize_url": null, "lang": "en", "start_date": "2026-07-09", "end_date": "2026-07-09", "start_time": "10:00:00", "end_time": "12:00:00", "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Nicolas Flammarion<br>\r\nThesis advisor: Prof. Pascal Fua<br>\r\nCo-examiner: Prof. Alexander Mathis<br>\r\n<br>\r\n<u>Abstract</u><br>\r\nMany high-impact problems in science and engineering reduce to optimizing a complex objective, where each evaluation is costly and often unreliable: physical experiments fail, simulators diverge, and stochastic systems return noisy feedback. Classical black-box optimizers struggle in this regime, where evaluation budgets are tight and the feasible region is hard to characterize a priori. Our work investigates how the statistical expressivity of modern generative samplers can be leveraged in black-box optimization. We propose that these samplers enable efficient navigation of feasible sets and optimization of geometrically complex objectives under limited, unreliable observations. <br>\r\n<br>\r\nTwo complementary lines of work have already been studied. The main one, SPARROW, demonstrated that a sampler-driven optimizer can substantially reduce the number of function calls needed to maximize complex and unreliable objectives. A parallel line established that samplers can be steered to satisfy hard constraints without sacrificing the statistical diversity of the generated samples. Because SPARROW imposes minimal assumptions on the underlying sampler, these constrained samplers can drive it directly, opening a path to constrained black-box optimization.<br>\r\n<br>\r\nBuilding on these results, two directions are planned. The first is theoretical: a deeper mathematical study of sampler-driven optimization, drawing on the broader literature on sample-efficient black-box optimization, in order to derive stronger guarantees and more efficient algorithms. The second is applied: applying the methods to concrete domains and exploiting the structure those domains expose to sharpen performance beyond what a pure black-box treatment can achieve.<br>\r\n<br>\r\nThe applications of low-budget black-box optimization are diverse, including engineering design (e.g. aerodynamic shape optimization, mechanical components), scientific discovery (e.g. protein and molecule design under synthesizability and stability constraints), and the safety and security of opaque ML systems (e.g. query-efficient adversarial probing, red-teaming of black-box models).<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\n ", "image_description": "", "creation_date": "2026-05-22T12:53:32", "last_modification_date": "2026-05-22T13:09:04", "link_label": "", "link_url": "", "canceled": "False", "cancel_reason": "", "place_and_room": "BC 333", "url_place_and_room": "https://plan.epfl.ch/?room==BC%20333", "url_online_room": "", "spoken_languages": [], "speaker": "Edouard Dufour", "organizer": "", "contact": "[email protected]", "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": "EDIC candidacy exam", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/120837/", "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/48/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 71000, "title": "Gestion de projet européen - Projets collaboratifs", "slug": "gestion-de-projet-europeen-projets-collaboratifs-2", "event_url": "https://memento.epfl.ch/event/gestion-de-projet-europeen-projets-collaboratifs-2", "visual_url": "https://memento.epfl.ch/image/32388/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32388/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32388/max-size.jpg", "lang": "en", "start_date": "2026-09-01", "end_date": "2026-09-01", "start_time": "09:30:00", "end_time": "12:00:00", "description": "<p>Programmes-cadres européens de la recherche et de l’innovation.<br>\r\n<br>\r\nVotre laboratoire participe à un ou plusieurs projets européens et une de vos missions est d’en assurer une gestion efficace au niveaux administratif, financier et celui des ressources humaines ? Pour cela, il est important de prendre le temps de vous familiariser avec la structure de ces projets et avec certaines règles de gestion qui leurs sont spécifiques.<br>\r\n<br>\r\nCette formation vous propose de prendre connaissance des documents contractuels et de leur contenu qui est utile pour votre gestion. Les règles de gestion administrative, financière et RH seront présentées et discutées en se basant sur des cas pratiques.<br>\r\nLa formation permettra aussi aux participants de se familiariser avec le portail européen et la plateforme de reporting du SEFRI.<br>\r\n<br>\r\nIl est recommandé de suivre cette formation le plus tôt possible, idéalement vers le commencement du projet.</p>", "image_description": "", "creation_date": "2026-01-30T10:08:17", "last_modification_date": "2026-01-30T10:10:37", "link_label": "Inscription", "link_url": "https://epfl.eu.crossknowledge.com/site/m/public_training/565", "canceled": "False", "cancel_reason": "", "place_and_room": "", "url_place_and_room": "", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/1/?format=api" ], "speaker": "EU-Funding Team of the Research Office", "organizer": "KeepLearning", "contact": "[email protected]", "is_internal": "True", "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/119523/", "category": { "id": 15, "code": "FORM", "fr_label": "Formations internes", "en_label": "Internal trainings", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/145/?format=api", "https://memento.epfl.ch/api/v1/mementos/431/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api" ] }, { "id": 71001, "title": "Gestion de projet européen - Projets ERC et MSCA", "slug": "gestion-de-projet-europeen-projets-erc-et-msca-2", "event_url": "https://memento.epfl.ch/event/gestion-de-projet-europeen-projets-erc-et-msca-2", "visual_url": "https://memento.epfl.ch/image/32389/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32389/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32389/max-size.jpg", "lang": "en", "start_date": "2026-11-26", "end_date": "2026-11-26", "start_time": "09:30:00", "end_time": "12:00:00", "description": "<p>Programmes-cadres européens de la recherche et de l’innovation.<br>\r\n<br>\r\nVotre laboratoire participe à un ou plusieurs projets européens et une de vos missions est d’en assurer une gestion efficace au niveaux administratif, financier et celui des ressources humaines ? Pour cela, il est important de prendre le temps de vous familiariser avec la structure de ces projets et avec certaines règles de gestion qui leurs sont spécifiques.<br>\r\n<br>\r\nCette formation vous propose de prendre connaissance des documents contractuels et de leur contenu qui est utile pour votre gestion. Les règles de gestion administrative, financière et RH seront présentées et discutées en se basant sur des cas pratiques.<br>\r\nLa formation permettra aussi aux participants de se familiariser avec le portail européen et la plateforme de reporting du SEFRI.<br>\r\n<br>\r\nIl est recommandé de suivre cette formation le plus tôt possible, idéalement vers le commencement du projet.</p>", "image_description": "", "creation_date": "2026-01-30T10:11:14", "last_modification_date": "2026-01-30T10:12:40", "link_label": "Inscription", "link_url": "https://epfl.eu.crossknowledge.com/site/m/public_training/565", "canceled": "False", "cancel_reason": "", "place_and_room": "", "url_place_and_room": "", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/1/?format=api" ], "speaker": "EU-Funding Team of the Research Office", "organizer": "KeepLearning", "contact": "[email protected]", "is_internal": "True", "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/119525/", "category": { "id": 15, "code": "FORM", "fr_label": "Formations internes", "en_label": "Internal trainings", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/145/?format=api", "https://memento.epfl.ch/api/v1/mementos/431/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api" ] }, { "id": 71505, "title": "Gräff & ... Labs", "slug": "graff-labs", "event_url": "https://memento.epfl.ch/event/graff-labs", "visual_url": "https://memento.epfl.ch/image/32844/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32844/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32844/max-size.jpg", "lang": "en", "start_date": "2026-11-04", "end_date": "2026-11-04", "start_time": "12:15:00", "end_time": "13:15:00", "description": "<em>Hybrid - By invitation only</em><br>\r\n<br>\r\nGräff Lab: Gabriel Berdugo-Vega <br>\r\n<br>\r\nTitle:<br>\r\n<br>\r\n ", "image_description": "", "creation_date": "2026-03-31T15:20:29", "last_modification_date": "2026-05-15T13:16:13", "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": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "Gabriel Berdugo-Vega & ...", "organizer": "SV BMI", "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/120250/", "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/19/?format=api" ] }, { "id": 71853, "title": "HSG START Accelerator Batch #3", "slug": "hsg-start-accelerator-batch-3-2", "event_url": "https://memento.epfl.ch/event/hsg-start-accelerator-batch-3-2", "visual_url": "https://memento.epfl.ch/image/33174/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/33174/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/33174/max-size.jpg", "lang": "en", "start_date": "2026-05-29", "end_date": "2026-05-29", "start_time": "11:00:00", "end_time": "12:00:00", "description": "<strong>HSG START Accelerator – Three Months to Become Investor‑Ready</strong><br>\r\n<br>\r\nThe <a href=\"https://www.hsgstartaccelerator.org/program\"><strong>HSG START Accelerator</strong></a> is a <strong>3‑month (September to December 2026), high‑intensity program</strong> in St. Gallen designed to help early‑stage tech and deeptech startups become fully <strong>investor‑ready</strong>. Run by the <a href=\"https://www.linkedin.com/company/university-of-st-gallen/\">University of St. Gallen</a>, the <a href=\"https://www.linkedin.com/company/start-foundation-ch/\">START Foundation</a>, and the <a href=\"https://www.linkedin.com/company/switzerland-innovation-park-ost/\">Switzerland Innovation Park Ost</a>, it combines strategic coaching, expert mentoring, and direct exposure to investors across Europe.<br>\r\n<br>\r\n<strong>What the program provides</strong>\r\n<ul>\r\n\t<li><strong>CHF 15,000 non‑dilutive grant</strong>, with access to <strong>up to CHF 200,000</strong> in additional funding.</li>\r\n\t<li><strong>Tailored coaching</strong> from HSG experts and experienced founders.</li>\r\n\t<li><strong>Weekly mentoring</strong> with investors, operators, and industry specialists.</li>\r\n\t<li><strong>Core sessions</strong> including Bootcamp, Expert Sessions, Roasting Sessions, Mock Office Hours, and Demo Day.</li>\r\n\t<li><strong>On‑site participation in St. Gallen</strong>, with dedicated coworking space.</li>\r\n</ul>\r\n<strong>Who it’s for</strong><br>\r\n<br>\r\nStartups in <strong>deeptech, AI, MedTech, ClimaTech, robotics</strong>, and emerging technologies, typically coming from leading institutions (ETH, EPFL, Empa, HSG, etc.) and preparing for their next growth and fundraising milestones.<br>\r\n<br>\r\n<strong>Program impact</strong><br>\r\n<br>\r\nAlumni consistently report sharper market positioning, stronger business models, and high‑value investor connections, making the program a recognized launchpad for ambitious European founders.<br>\r\n<br>\r\n<strong>Application deadline</strong><br>\r\n<br>\r\n29 May 2026<br>\r\n<a href=\"https://form.typeform.com/to/CnxTKS8K?typeform-source=www.hsgstartaccelerator.org\">Apply now</a>", "image_description": "", "creation_date": "2026-05-12T16:11:21", "last_modification_date": "2026-05-12T16:11:35", "link_label": "webpage", "link_url": "https://www.hsgstartaccelerator.org/program", "canceled": "False", "cancel_reason": "", "place_and_room": "", "url_place_and_room": "", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<a href=\"https://www.linkedin.com/company/university-of-st-gallen/\">HSG START Accelerator </a>", "contact": "<a href=\"mailto:[email protected]\">Oliver Sabathy</a>", "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": "Startup; call for application; deeptech; funding", "file": "https://memento.epfl.ch/public/upload/files/ApplicationBatch3FlyerEPFL.pdf", "icalendar_url": "https://memento.epfl.ch/event/export/120727/", "category": { "id": 17, "code": "DEADLINE", "fr_label": "Echéance", "en_label": "Deadline", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/417/?format=api", "https://memento.epfl.ch/api/v1/mementos/422/?format=api" ] } ] }
