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dynamics (MD) simulations including enhanced sampling techniques as well as machine learning. Profile Applicants should hold a M.Sc. in computational chemistry, chemistry, biochemistry, or physics
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can significantly improve the treatment and outcome of oncological patients. Project background You will contribute to the design and implementation of machine-learning-based sparse 3D image
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methods involving molecular surface display and deep sequencing to study force-dependent behavior in protein systems. These datasets will, in turn, be used to train machine learning models capable
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only supports your professional development, but also actively contributes to positive change in society You can expect numerous benefits, such as public transport season tickets and car sharing, a wide
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PhD in one of these areas. Candidates should have an exceptional academic record, a strong background in machine learning, and a robust mathematical foundation. They are also expected to have strong
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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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to ETH's central compute and the Alps supercomputer at CSCS A multidisciplinary team of mechanical engineers, materials scientists, biologists, and machine learning researchers, embedded in the broader
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develop novel learning-based control and policy optimization techniques. We're looking for a skilled machine learning (ML) engineer to develop cutting-edge AI algorithms for digital twin applications in
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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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, materials scientists, biologists, and machine learning researchers, embedded in the broader robotics ecosystem at ETH Zurich Strong ties to industry and to our spin-offs, and support for turning your research