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processing, machine learning, and human-computer interaction. The goal of this PhD is to develop neuromotor interfaces for dexterous robot teleoperation using two sensing modalities: 1) surface
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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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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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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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, 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
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study design, scientific methods, and statistical modelling Python, C# or equivalent language expertise Basic knowledge of machine learning or deep learning Desirable Skills Exposure to musculoskeletal
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reconstruction, data inference and fusion using classical and physics-informed machine learning approaches to advance time-resolved morphological and tissue property imaging of the heart The work will involve