93 algorithm-development-"Multiple"-"Prof"-"UNIS" Postdoctoral positions at Princeton University
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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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degree (or expect to receive a PhD degree by June 15, 2026) in Psychology with demonstrated expertise in gender development. Applicants should have expertise in video coding (e.g., Datavyu), longitudinal
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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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. This mission is pursued and supports the University's purpose by using current knowledge of health and human development to guide responsive, high quality clinical, prevention, and consultation services. UHS's
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simultaneous recordings and stimulation from multiple, interconnected brain regions. The researcher will gain experience with the use of laminar/neuropixel probes and electrical microstimulation to study
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The condensed matter spectroscopy group at Princeton University invites applications for multiple Postdoctoral Research or more senior positions to work in experimental condensed matter physics with
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-microenvironment interactions during cancer progression. Ludwig Princeton Branch is dedicated to accelerating the study of metabolic phenomena associated with cancer to develop new paradigms for cancer prevention
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) with expertise and interest in Large Language Models (LLM) for Energy Environmental Research and Applications. The researcher(s) will work with the principal investigator and team to develop, fine tune
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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