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Quantitative Finance at EPFL seeks postdocs to reinforce our research in quantitative finance with a focus on applied machine learning. Main duties and responsibilities Working and collaborating on research
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conducts applied and theoretical research on the economics and politics of sustainable development, as well as the policies to achieve it. More information at: https://www.epfl.ch/labs/pasu/ . We are
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technology transfer activities Profile PhD in microengineering, electrical engineering, mechanical engineering, applied physics, materials science, or a closely related field Strong hands-on experience
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learned control policies and neural representations of movement in collaboration with experimental labs Profile Common requirements: PhD (completed or close to completion) in computer science, applied
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wavefront shaping methods based on adaptive optics applied to the context of nonlinear laser-matter interaction. Main duties and responsibilities Define relevant research and experimental plans
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a related quantitative field Experience with machine learning and/or statistical modeling applied to biological data Proven expertise in single-cell data analysis (scRNA-seq and/or scATAC-seq
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candidates with A PhD in machine learning, computer science, applied mathematics, statistics, physics, engineering, or a closely related field, completed or near completion. Demonstrated expertise in
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Engineering. The successful candidate will conduct cutting-edge research in applied and environmental microbiology, with a particular focus on microbial community assembly and interaction principles using
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intersection of applied physics, high-performance computing, and semiconductor metrology. The successful candidate will have the opportunity to develop their own independent research direction and work
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member of the SNSF Starting Grant project NEXUS-3D, working at the intersection of applied physics, high-performance computing, and semiconductor metrology. The successful candidate will have the