77 algorithm-development-"Multiple"-"Prof" "UNIS" Postdoctoral positions at Princeton University
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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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. '74 Walton III Senior Research Scientist. The research is highly applied in nature, and will involve a granular exploration of the sequence of development, investment decision making, financing, and
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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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; monetary and regulatory policies; cooperation at the global and regional level; the domestic and international politics of economic development; the political economy of human security; the evolution and
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successful candidate will develop and apply computational approaches to chemical datasets, with artificial intelligence/machine learning (AI/ML) being a major focus. Many of the laboratory's interests center
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) for Energy & Environmental Research and Applications. The researcher(s) will work with the principal investigator and team to develop, fine tune, and deploy LLM based tools for environmental engineering
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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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their departments and can acquire a breadth of expertise by working with multiple faculty members. We value building a culturally diverse intellectual community; women and members of underrepresented groups
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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