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, biochemistry, computer science, biological or chemical engineering, forensic science, or a related field. To apply online, please visit https://puwebp.princeton.edu/AcadHire/position/38881 and submit a CV and
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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of terrestrial vertebrates on Earth today with 11,000 species. A Ph.D. in Evolutionary Biology, Computational Biology, or related fields, is required. The work will focus on i) phylogenomic inference of hundreds
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, including but not limited to cement chemistry, material science, or structural materials and mechanics. Candidates with a strong commitment to interdisciplinary research are especially encouraged to apply
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. Candidates who are nearing completion of their Ph.D. (i.e. with a confirmed defense date) or hold a Ph.D. in chemical engineering, chemistry, materials science and engineering, mechanical engineering, or a
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multidisciplinary approach comprising immunology, cell biology, developmental biology and computational biology to complement our primary tools from intravital microscopy. The postdoctoral scholar will have direct
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are intended for early-career scientists with a research interest in data science, statistics, and machine learning. As an associate, you will join the research group of a current CSML participating faculty
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-profit organization that uses science and technology to help preserve endangered species and ecosystems. Candidates are required to have a Ph.D. or equivalent in evolutionary biology, genomics, or related
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The Rosen Research Group at Princeton University (https://rosen.cbe.princeton.edu) is searching for a postdoctoral or more senior researcher interested in computational materials design and
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differential equations, computational fluid dynamics, material science, dynamical systems, numerical analysis, stochastic analysis, graph theory and applications, mathematical biology, financial mathematics