16 informatics "https:" "https:" "https:" "https:" Postdoctoral positions at Princeton University
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discovery from small molecule mass spectrometry (e.g., https://www.nature.com/articles/s41586-025-09969-x, https://www.nature.com/articles/s42256-021-00407-x, https://www.nature.com/articles/s42256-024-00821
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breadth of disciplines - from genomics to computer science, sociology to psychology, engineering to environmental studies - to make novel insights and to tackle the full complexity of human health. We seek
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Computer Science at Princeton University, with the goal of developing next-generation machine-learningâ“based interatomic potentials (MLPs) for Earth and planetary materials across extreme pressureâ“temperature
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, computer science, and genetics. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding
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backgrounds can afford a Princeton education. Connections working at Princeton University More Jobs from This Employer https://main.hercjobs.org/jobs/22428141/postdoctoral-research-associate-tokamak-physics-and
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data-driven, computational approaches. Successful candidates will be willing and able to work across a breadth of disciplines - from genomics to computer science, sociology to psychology, engineering to
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the social sciences, statistics, computer science, or related fields, and their interests must fall at the technical forefront of quantitative social science. Candidates should offer state-of-the-art
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https://main.hercjobs.org/jobs/22428128/postdoctoral-research-associate Return to Search Results
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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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of existing AI/ML tools to assign structures to unidentified peaks in metabolomic datasets (e.g., https://www.nature.com/articles/s42256-021-00407-x ), and extending these tools or developing new models as