88 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof" "UNIS" Postdoctoral positions at Princeton University
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technologies enabling the development of property, and the religious and moral aspects of poverty and ownership. Intellectual, environmental, and economic historians, as well as historians of art, gender, race
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with managing the lab and projects. We also expect that you will collaborate with the ARG team on developing grant proposals.QualificationsRequired qualifications:Doctoral degree in a related field, such
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collaborate with the ARG team on developing grant proposals.QualificationsRequired qualifications:Doctoral degree in a related field, such as Architecture, Civil Engineering, Robotics, etc.Excellent track
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of the Princeton Precision Health (PPH) initiative is to revolutionize the understanding and advancement of human health by conducting interdisciplinary foundational research, developing and harnessing advanced
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project, the appointee will have opportunities to develop additional projects with members of Dr. Sinclair's lab and/or maintain their on-going work. The work location for this position is in-person
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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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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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commitment to interdisciplinary research are especially encouraged to apply. Responsibilities will include: - Developing a computational Artificial Intelligence form finding design framework to shape
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on i) phylogenomic inference of hundreds of whole genomic data already available; and ii) investigating rates of evolution across the genome and their correlation with phenotypic traits across various
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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