Eine App programmieren? Ein Computerspiel erfinden? Eine Animation gestalten?
\r\nWenn dich eines dieser Dinge interessiert, bist du hier am richtigen Ort! Tausche und teile deine Ideen, lerne zu programmieren und entdecke Informatikberufe. Die Coding Club for Girls Workshops sind für Mädchen im Alter von 11 bis 15 Jahren.
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\r\nDie Workshops können individuell besucht werden und die Teilnahme ist kostenlos und werden auf Deutsch angeboten.
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\r\nAb der Teilnahme an drei Workshops pro Jahr in Freiburg wird eine Teilnahmebestätigung ausgehändigt.
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\r\nOrt: Cycle d'Orientation de Pérolles (CO), Freiburg
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\r\nDatum und Zeit:
\r\n21. März 2026 : 09:30 bis 12:00 oder 13:30 bis 16:00 Uhr // Hello 3D World
\r\n25. April 2026 : 09:30 bis 12:00 oder 13:30 bis 16:00 Uhr // GameDesign
\r\n09. Mai 2026 : 09:30 bis 12:00 oder 13:30 bis 16:00 Uhr // Cyber Security
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\r\nDie Inhalte der Workshops sind untenstehend beschrieben.
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\r\nBedingungen\r\n
The 2027 edition of the CROSS program calls upon researchers at EPFL and the University of Lausanne to submit proposals for joint projects that bring the natural sciences and engineering together with the social sciences and humanities.
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\r\nThrough this annual call for projects, CROSS provides competitive grants to support new seed research endeavors that have the potential to grow into full–scale interdisciplinary research projects.
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\r\nThe 2024 CROSS call is open to all topics.
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\r\nPROPOSALS:
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\r\nProposals must come from joint UNIL-EPFL teams, which bring together specialists in the human and social sciences on the one hand, with specialists from life sciences, natural sciences or engineering on the other. Up to six projects will be selected, with a maximum of CHF 60,000 awarded per project.\r\n
The Sustainable Buildings and Construction Summit 2026 (20-22 April, Lausanne, Switzerland) convenes government officials alongside academics, private sector leaders, financiers, and civil society—converging policymakers, researchers, and practitioners to translate global sustainability commitments into practical solutions. Co-organised by EPFL and the UNEP-hosted Global Alliance for Buildings and Construction, the Summit aims to accelerate the transition to a resilient built environment worldwide, with particular focus on emerging markets and developing economies where built environment growth is most significant.The Summit will include thought-evoking keynotes, cross-disciplinary workshops, academic showcases, and solution-focused roundtables on themes such as upfront embodied emissions, climate resilient construction, circularity, affordable housing, and sustainable urban planning.
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\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.
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\r\nDescription
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\r\nBig-data-driven methodologies have emerged as a fundamental paradigm of science, but require an enormous amount of resources to achieve their promised impact. The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles [1] ensure that scientific data can be shared and reutilized, providing an efficient route for accumulating data and taking advantage of these powerful techniques. FAIR data management allows essential knowledge to be systematically extracted from data, accelerating discoveries and innovations across various domains [2]. Furthermore, open science is essential for the verifiability and reproducibility of results and has been a topic of major discussion over the last decade. In materials science, data-driven methodologies, coupled with the appropriate FAIR data management practices, are invaluable for the discovery of new materials due to the vast combinatorial space of chemical systems that emerge from the periodic table [3, 4]. Such methodologies have been successfully applied, e.g., to design and predict new materials with desired properties using ab-initio ground state simulations, i.e., data generated from Density Functional Theory (DFT) calculations [5]. However, there remains a critical gap in replicating this success in the context of other simulation frameworks.
\r\nTheoretical spectroscopy and Green's function method simulations [6, 7], including data simulated using the GW approximation, Time-Dependent Density Functional Theory (TDDFT), the Bethe-Salpeter equation (BSE), Dynamical Mean-Field Theory (DMFT), and Korringa-Kohn-Rostoker (KKR), pose especially difficult challenges in the context of FAIR data management. These simulations not only involve extensive computational resources and produce large datasets with associated complex workflows but are also executed using a large variety of public and in-house simulation software. At the same time, these methodologies are essential for understanding excited state properties of complex materials; they are more accurate than DFT calculations and provide better comparisons with experimental results since they incorporate excited states and electronic correlation effects in a more consistent manner [8].
\r\nThere has recently been a number of individual efforts to improve the accessibility of data produced by theoretical spectroscopy and Green’s function methods through the usage of publicly accessible databases. For example, the Computational Materials Repository (CMR) [9] contains several individual databases, amongst which the Computational 2D Materials Database (C2DB) [10] contains GW and BSE data for a specific set of parameters and properties. The MaterialsCloud [11] database has some individual datasets published for these methodologies, however there is not a clear data structure for them. The NIST-JARVIS [12] database has a specific app for BeyondDFT simulations with DMFT data, but only for a specific simulation code. By making datasets findable, these efforts aim to avoid redundant computations and thus build upon existing work more efficiently. While these efforts represent an important step in the right direction, they fall short of fully achieving their goal due to a continued lack of consistency (i.e., interoperability) between individual databases. Moreover, these self-managed databases typically lack the ability to store the complete provenance of the simulated workflow, which is essential to ensure reproducibility.
\r\nRecently, FAIRmat [13], a consortium of the German research data infrastructure (NFDI) association, was formed to construct a scalable data infrastructure for Materials Science that can be easily customized for individual communities. This infrastructure consists of a primary software and repository called NOMAD [14]—a free web-service that enables the organization, analysis, sharing, and publishing of materials science data. One of the tasks within FAIRmat’s scope is to build support for theoretical spectroscopy and Green’s function simulations within NOMAD. Support for several of these methodologies have now been successfully built, and there already exists over 10 000 entries in the NOMAD repository containing GW [15], BSE [16], and DMFT [17] data, along with the full provenance of the corresponding complex workflows. The next step to developing a FAIR data infrastructure for these methods is to tackle the interoperability problem.
\r\nInteroperability within this domain is extremely challenging due to the heterogeneous character of theoretical spectroscopy and Green’s function simulations. Consequently, the adoption of common structures (e.g., describing the Green’s function, the self-energy, or the dielectric function) is the key for improving interoperability. Thus, various members of the community, including method developers, materials and data scientists, and data management experts, must come together to reach a consensus on specific common data structures.
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\r\nReferences
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\r\n[1] Computational Materials Repository (CMR) website
\r\n[2] S. Di Cataldo, P. Worm, J. Tomczak, L. Si, K. Held, Nat. Commun., 15, 3952 (2024)
\r\n[3] F. Meng, B. Maurer, F. Peschel, S. Selcuk, M. Hybertsen, X. Qu, C. Vorwerk, C. Draxl, J. Vinson, D. Lu, Phys. Rev. Materials, 8, 013801 (2024)
\r\n[4] M. van Setten, F. Caruso, S. Sharifzadeh, X. Ren, M. Scheffler, F. Liu, J. Lischner, L. Lin, J. Deslippe, S. Louie, C. Yang, F. Weigend, J. Neaton, F. Evers, P. Rinke, J. Chem. Theory Comput., 11, 5665-5687 (2015)
\r\n[5] M. Scheidgen, L. Himanen, A. Ladines, D. Sikter, M. Nakhaee, Á. Fekete, T. Chang, A. Golparvar, J. Márquez, S. Brockhauser, S. Brückner, L. Ghiringhelli, F. Dietrich, D. Lehmberg, T. Denell, A. Albino, H. Näsström, S. Shabih, F. Dobener, M. Kühbach, R. Mozumder, J. Rudzinski, N. Daelman, J. Pizarro, M. Kuban, C. Salazar, P. Ondračka, H. Bungartz, C. Draxl, JOSS., 8, 5388 (2023)
\r\n[6] FAIRmat website
\r\n[7] K. Choudhary, K. Garrity, A. Reid, B. DeCost, A. Biacchi, A. Hight Walker, Z. Trautt, J. Hattrick-Simpers, A. Kusne, A. Centrone, A. Davydov, J. Jiang, R. Pachter, G. Cheon, E. Reed, A. Agrawal, X. Qian, V. Sharma, H. Zhuang, S. Kalinin, B. Sumpter, G. Pilania, P. Acar, S. Mandal, K. Haule, D. Vanderbilt, K. Rabe, F. Tavazza, npj. Comput. Mater., 6, 173 (2020)
\r\n[8] L. Talirz, S. Kumbhar, E. Passaro, A. Yakutovich, V. Granata, F. Gargiulo, M. Borelli, M. Uhrin, S. Huber, S. Zoupanos, C. Adorf, C. Andersen, O. Schütt, C. Pignedoli, D. Passerone, J. VandeVondele, T. Schulthess, B. Smit, G. Pizzi, N. Marzari, Sci. Data., 7, 299 (2020)
\r\n[9] S. Haastrup, M. Strange, M. Pandey, T. Deilmann, P. Schmidt, N. Hinsche, M. Gjerding, D. Torelli, P. Larsen, A. Riis-Jensen, J. Gath, K. Jacobsen, J. Jørgen Mortensen, T. Olsen, K. Thygesen, 2D Mater., 5, 042002 (2018)
\r\n[10] M. Wilkinson, M. Dumontier, I. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J. Boiten, L. da Silva Santos, P. Bourne, J. Bouwman, A. Brookes, T. Clark, M. Crosas, I. Dillo, O. Dumon, S. Edmunds, C. Evelo, R. Finkers, A. Gonzalez-Beltran, A. Gray, P. Groth, C. Goble, J. Grethe, J. Heringa, P. ’t Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S. Lusher, M. Martone, A. Mons, A. Packer, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S. Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, M. Swertz, M. Thompson, J. van der Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wittenburg, K. Wolstencroft, J. Zhao, B. Mons, Sci. Data., 3, 160018 (2016)
\r\n[11] L. Reining et al., Comptes Rendus Physique 10, 6 (2009)
\r\n[12] V. Blum, R. Asahi, J. Autschbach, C. Bannwarth, G. Bihlmayer, S. Blügel, L. Burns, T. Crawford, W. Dawson, W. de Jong, C. Draxl, C. Filippi, L. Genovese, P. Giannozzi, N. Govind, S. Hammes-Schiffer, J. Hammond, B. Hourahine, A. Jain, Y. Kanai, P. Kent, A. Larsen, S. Lehtola, X. Li, R. Lindh, S. Maeda, N. Makri, J. Moussa, T. Nakajima, J. Nash, M. Oliveira, P. Patel, G. Pizzi, G. Pourtois, B. Pritchard, E. Rabani, M. Reiher, L. Reining, X. Ren, M. Rossi, H. Schlegel, N. Seriani, L. Slipchenko, A. Thom, E. Valeev, B. Van Troeye, L. Visscher, V. Vlcek, H. Werner, D. Williams-Young, T. Windus, Electron. Struct., (2024)
\r\n[13] L. Ghiringhelli, C. Baldauf, T. Bereau, S. Brockhauser, C. Carbogno, J. Chamanara, S. Cozzini, S. Curtarolo, C. Draxl, S. Dwaraknath, Á. Fekete, J. Kermode, C. Koch, M. Kühbach, A. Ladines, P. Lambrix, M. Himmer, S. Levchenko, M. Oliveira, A. Michalchuk, R. Miller, B. Onat, P. Pavone, G. Pizzi, B. Regler, G. Rignanese, J. Schaarschmidt, M. Scheidgen, A. Schneidewind, T. Sheveleva, C. Su, D. Usvyat, O. Valsson, C. Wöll, M. Scheffler, Sci. Data., 10, 626 (2023)
\r\n[14] J. Schmidt, M. Marques, S. Botti, M. Marques, npj. Comput. Mater., 5, 83 (2019)
\r\n[15] L. Himanen, A. Geurts, A. Foster, P. Rinke, Advanced Science, 6, (2019)
\r\n[16] M. Scheffler, M. Aeschlimann, M. Albrecht, T. Bereau, H. Bungartz, C. Felser, M. Greiner, A. Groß, C. Koch, K. Kremer, W. Nagel, M. Scheidgen, C. Wöll, C. Draxl, Nature, 604, 635-642 (2022)
\r\n[17] C. Draxl, M. Scheffler, MRS Bull., 43, 676-682 (2018)
Hosted by Frédéric Grouiller, CIBM MRI HUG-UNIGE Section head, we are pleased to invite you to attend the CIBM 7T MRI Seminar on April 21st at 10:00 CEST by Caroline Le Ster from the CEA Neurospin, Paris who will be sharing on “Leveraging the potential of UHF scanners for fMRI studies”.
\r\nAbstract
\r\nThere is a growing availability of 7T scanners, with approximately 100 systems currently installed worldwide. These ultra-high-filed (UHF) scanners are particularly relevant for neuroimaging, where anatomical and functional images can be acquired with higher spatial and temporal resolution than on conventional scanners. However, UHF scanners remain difficult to operate, and understanding their specific limitations is essential. These systems indeed face several challenges arising from MR physics and biological effects that must be addressed within engineering constraints. Leverage the potential of UHF scanners, for instance through field monitoring, is therefore important to make the most of these scanners and benefit from the intrinsic SNR gain.
\r\nfMRI particularly benefits from the increase in magnetic field strength, enabling studies to be performed at higher resolution than at lower fields. In order to maintain reasonable acquisition times despite larger imaging matrices, several acceleration strategies have been developed, e.g. multi-slice EPI (SMS-EPI), three-dimensional EPI (3D-EPI) and non-Cartesian sampling schemes. In addition, functional contrasts also benefit from the field increase, including improved spatial localization of the BOLD contrast and the possibility to exploit VASO contrast.
For more than two decades leading professional organizations have called for a reform of the mathematics education of biology and life sciences students. Reflecting the growing importance of mathematics in life sciences research, such reform should bridge the gap between the mathematics students learn and the mathematics used in research.
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\r\nIn this talk, Steve Bennoun, UCLA, will present how the study of modeling and dynamical systems can be used to create a first-year course that introduces mathematical concepts used in life sciences research in a contextualized way.
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\r\nStudents first learn to write models for important biological systems such as hormone levels, epidemiological models, and predator-prey models.
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\r\nTaking a geometrical approach, students learn how to determine the long-term behaviors of models by studying phase plane trajectories and the stability of equilibrium points.
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\r\nThis enables the study of the concept of “biological switch” or how a cell can start or stop the production of a protein. This is followed by the important notion of qualitative change in a biological systems, or bifurcation.
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\r\nStudents learn in particular about Hopf bifurcations or how a system can start or stop oscillating. The second part of the course moves somewhat away from the geometrical approach and focuses on how to determine the stability of equilibrium points using the linearization of a model, motivating the study of the necessary notions from linear algebra by studying discrete-time models.
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\r\nThis talk will end with a discussion focusing on research by Sanders et al. (2021) suggesting that such a course supports students success and can reduce equity gaps.
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\r\nlunch&LEARN sessions are intended for anyone who teaches at EPFL.
\r\nThis session is hybrid and can be attended in person or followed via zoom.
\r\nMost sessions are recorded and can be found on the Center LEARN's YouTube channel.
\r\nTo get in touch with the team, to suggest a session, or to make inquiries, please send an email to lunchLEARN@groupes.epfl.ch