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://pritykinlab.princeton.edu) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi-modal single-cell, spatial and genome editing
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postdoctoral research position available in the area of phylogenomics to work with Professor Tiago Simes. The Simes lab is broadly interested in phylogenetic methods and applications, using morphological and
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develop methods for the synthesis and analysis of systems producing renewable fuels and chemicals; and use these methods, in collaboration with other researchers at Princeton and other institutions
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with the possibility of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. Candidates must have or (or expect to have) a
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, analysis, and optimization. The goal of the project is to develop methods for the synthesis and analysis of systems producing renewable fuels and chemicals; and use these methods, in collaboration with other
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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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performance and continued funding. For details on specific faculty members and their research interests, please review the Faculty Research Interests document. https://www.pacm.princeton.edu/sites/default/files
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technologies. The Pritykin lab (http://pritykinlab.princeton.edu ) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi
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on social vulnerability to hazards. The researcher will have the opportunity to work on multiple projects, investigating (a) cumulative environmental impacts, (b) the use of census microdata for social
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nano-plasmonics) design, fabrication, and characterizations. All candidates should have a Ph.D. degree. Appointments will be for one year, with the possibility of renewal pending satisfactory performance