This workshop is for staff and researchers working in NGOs and non-profits who aim to bring AI into their workflows with an ethical, careful approach. Join us for a hands-on morning of using and deploying the flagship model of the Swiss AI Initiative, learn first hand from the engineers and scientists working on Apertus.
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\r\nThe workshop runs from 9:30 to 12:30 at the EPFL AI Center Lounge in Lausanne. Doors open at 9:00.
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\r\nBackground: Apertus is a fully open large language model built in Switzerland, which is fully reproducible and can run on your own infrastructure. As a Foundation Model, it has already been adapted to specific contexts through post-training and fine-tuning processes. In addition to open access at all Swiss academic institutions, a growing number of Swiss and international commercial providers support Apertus for production deployment.
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\r\nThis workshop is also designed as a two-way conversation: alongside learning how to use and deploy Apertus, we want to hear how your organization is already working with AI, and where today's tools still fall short for you. We would like to hear about the languages, legal contexts, and use cases you operate in. This input will give us the opportunity to prioritize improvements for non-profit organizations on the Apertus roadmap.
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\r\nAt this workshop, you will have the chance to:\r\n
Components, measurements, wiring, and first circuit builds: everything you need to quickly go from idea to circuit!
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\r\nComputer and Communication Sciences doctoral program
\r\nThesis Nr. 11792
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\r\nTo take part in the public defense, please contact directly the speaker
Biological systems have already paved several routes for bioinspired materials, e.g., for structural colours, wetting, adhesion, and mechanical properties. They have largely involved equilibrium or kinetically trapped properties. By contrast, biological systems allowing multifunctional properties are characteristically dissipatively and dynamically adaptive and incorporate homeostatic feedback mechanisms to regulate the dynamic states. They also typically use signal transduction to mediate the signal from the sensory elements to the responsive sites, unlike the classic man-made stimuli responsive materials. These concept suggest avenues towards more complex life-like functions in soft matter. Herein, optically driven hydrogel systems are presented to drive homeostatically controlled thermal oscillations and to mediate signal transduction via thermal intermediate signalling (1). On the other hand, biological magnetoreception in elasmobranch organisms involves electromagnetic induction (EMI) and suggests avenues for bioinspired electrical signal transductions. We show magnetic profile recognition of translocated magnetically encoded soft matter objects using electrical intermediate signaling by EMI and decoding the magnetic information with machine learning (2). Combination of high stiffness and promoted dynamics for self-healing has turned challenging to combine. Self-healing hydrogels, still combining high tensile stiffness close to that of skin were shown by using polymers upon nanoconfinement in 2D-nanosheet layered confinements (3). Finally, we show trainable actuations in bilayer gel actuators (4). Ever more complex responses can be created, such as mimicking the Pavlovian classical conditioning (5), all paving ways for growingly life-like functions for soft robotics and interfacing with biology.
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\r\n1. H. Zhang, H. Zeng, A. Eklund, H. Guo, A. Priimagi, O. Ikkala, Feedback-controlled hydrogels with homeostatic oscillations and dissipative signal transduction, Nat. Nano, 17, 1303 (2022).
\r\n2. Z. Miao, X. Hu, K. Liu, S. Hu, G. Yan, H. Tan, O. Ikkala, Z. P. Lv, B. Peng, Bioinspired sensory transduction for magnetic profile recognition and encryption, Adv. Mat. 38 e15994 (2026).
\r\n3. C. Liang, V. Dudko, O. Khoruzhenko, X. Hong, Z.-P. Lv, J. Breu, O. Ikkala, H. Zhang, Stiff and self-healing hydrogels by polymer entanglements in co-planar nanoconfinement, Nat. Mater. 24, 599 (2025).
\r\n4. S. Hu, Y. Fang, C. Liang, M. Turunen, O. Ikkala, H. Zhang, Thermally trainable dual network hydrogels, Nat. Commun., 14, 3717 (2023).
\r\n5. H. Zhang, H. Zeng, A. Priimagi, O. Ikkala, Programmable responsive hydrogels with classical conditioning algorithm, Nat. Commun., 10, 3267 (2019).
Thesis Director: Prof. D. Constam,
\r\nMolecular Life Sciences doctoral program
\r\nThesis Nr. 11085
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\r\nTo take part in the public defense, please contact directly the speaker
Thesis Director: Prof. P. Scarlino,
\r\nPhysics doctoral program
\r\nThesis Nr. 11801
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\r\nTo take part in the public defense, please contact directly the speaker
Thesis Directors: Prof. J.-P. R. Kneib, Prof. R. I. Anderson
\r\nPhysics doctoral program
\r\nThesis Nr. 11973
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\r\nTo take part in the public defense, please contact directly the speaker
Are you a student or researcher interested in entrepreneurship? Whether you already have a technology or problem you want to explore, are looking for co-founders, or simply want to discover what building a startup could look like, Talent Kick can help you take the next step.
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\r\nTalent Kick is a free, cross-university entrepreneurship program that supports students and researchers in exploring promising ideas and technologies, finding co-founders and developing a startup alongside their studies.
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\r\nParticipants can benefit from:\r\n
Generative AI has become a part of students' everyday working life. If used without guidance, GenAI can cause over-reliance and leave students with a distorted sense of what they have actually learned. Most research and practice have focused on how we can scaffold students' GenAI use, for example through prompts, guardrails, or redesigned tasks. This talk asks a simpler question that has received far less attention. When should students be allowed to use AI so that it helps their learning?
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\r\nIn this lunch&LEARN session, Janne Rotter turns to a question that's been largely overlooked: not how, but when should students be allowed to use AI for it to actually support learning? Janne will present findings from a study of 105 university students comparing three conditions: unrestricted AI access, no access at all, and a \"timed\" condition where students could only turn to AI after first attempting the task on their own.
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\r\nDrawing on multiple pedagogical theories to build a decision rule for when access should open up, the results show that students in the timed condition outperformed those with unrestricted access on an objective post-test, and had a more accurate sense of their own understanding, without an explicit metacognitive prompting. Compared to withholding AI entirely, timed access also seemed to reduce task errors and time spent on task.
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\r\nJoin us to learn more about the topic and engage in further discussion.
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