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for personal and professional development Close collaboration between Empa and EPFL, bringing together complementary expertise in energy systems, machine learning, and foundation models The opportunity to shape
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, with the possibility of renewal, in the field of Analysis and Partial Differential Equations (PDEs). Job description - The successful candidate will collaborate on research projects related
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by Prof. Marc Gruber, within EPFL's College of Management of Technology. You will have scope to develop your own research agenda and to collaborate with Prof. Gruber and the Chair's researchers
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using machine learning approaches to uncover principles of cellular state transitions. The work will be carried out in close collaboration with other labs at EPFL, offering a uniquely rich environment
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research is built around three intertwined questions: how to measure and understand behavior with AI, how brains and embodied agents learn to control the body, and how the brain builds a sense of its body
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and industrial project partners. You will be responsible for: Developing and adapting machine-learning approaches for structure-based and generative molecular design. Integrating physicochemical
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on developing new generative modeling approaches, scalable training algorithms, and foundation model technologies. The role is suited for candidates with a strong machine learning background who are excited
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of Singapore, and EPFL (Switzerland). These partners are looking for talents in several domains of machine learning, AI, computational biology, and biology, to develop PhD theses across the main pillars
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adaptability at the product-scale through material science and design. Main duties and responsibilities As a research collaborator, you will be expected to: Conduct cutting-edge research including experimental
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and associated environmental impacts. Contribute to short-term (2026 to 2030) and long-term (2030 to 2050) verticalisation forecasting models based on machine learning, and to their validation against