114 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"UNIS"-"St" 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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; those hired at more senior ranks may have multi-year appointments. In addition to the aforementioned project, the appointee will have opportunities to develop additional projects with members of Dr
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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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organizations: High Meadows fellows are able to build on the knowledge and skills they developed at Princeton and find new opportunities to develop and extend their expertise. Their responsibilities have included
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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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project. The ideal candidate will have experience in yeast strain development and engineering CRISPR-based control of gene expression. This position will allow for both professional and laboratory skill
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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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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