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the Leverhulme Trust and the Wellcome Trust. The successful applicants will join the established and growing effort in biological physics within DAMTP. For further details about the group, please consult
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New York University Abu Dhabi, seeks to recruit a postdoctoral associate to work with Marwa Banna on one or more of the following topics: ? Random matrix theory ? Free probability theory ? High
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used in our work centre around optical imaging and spectroscopy and nanofabrication. The work also relies on theory and simulation, specifically focusing on numerical mean-field electrostatics
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, to study novel renewable energy technologies. The candidates are expected to have a PhD degree in Chemical Engineering or related field, and have experience with optimization (theory, modeling, and tools
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used in our work centre around optical imaging and spectroscopy and nanofabrication. The work also relies on theory and simulation, specifically focusing on numerical mean-field electrostatics
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 19 days ago
40 research groups and some 1,000 employees from over 50 nations, it is the largest institute of the Max Planck Society. The Department of Theoretical and Computational Biophysics headed by (Prof. Dr
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related field are particularly encouraged to apply.We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular
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optimization (theory, modeling, and tools). Candidates should apply at: https://www.princeton.edu/acad-positions/position/39361 and include a cover letter, CV (including a list of publications), research
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year with a potential renewal for a total of two years. We seek a candidate with strong optimization skills and expertise with game theory is a plus but not required. Ideally this person would have a
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About the Role The project “An Erlangen Programme for AI” (funded by the UKRI), will broadly involve applying advanced mathematical techniques for understanding training in neural networks, with