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at the Department of Biosystems Science and Engineering (D-BSSE) of ETH Zurich in Basel invites exceptional candidates to apply for a Postdoc position in pioneering projects at the intersection of synthetic biology
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. The researcher will gain an educational experience in multi-scale computational modeling and machine learning of mechanical behavior of high temperature alloys. They will be a part of interdisciplinary team and
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machine learning within the scope of ACAG. The postdoc will have various opportunities for professional development, including contributing to and leading grant proposal development (e.g., external
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will work under close supervision by the PI and our Research Scientist, as well as alongside students and postdocs in the lab. The technician will be expected to participate in group meetings and is a
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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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multidisciplinary environments Curiosity-driven and self-motivated working attitude Knowledge of biomechanical modeling, anatomy, vision-based motion capture, machine learning, control systems Keep in mind
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intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and scientific applications. About the research project You will work in Julian Togelius' new research
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analytical and/or computational facilities. The successful applicant will develop an active and outstanding externally funded research program, advise student and postdoc research, and teach classes
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
or equivalent experience in machine learning or a related quantitative field (Computer Science, Artificial Intelligence, Statistics, Mathematics, Physics, Computational Biology/Chemistry). Candidates will be
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equivalent foreign degree, obtained within the last three years prior to the application deadline Experience with simulation frameworks, system-level performance evaluation, or machine learning, is highly