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on projects related to machine-learning for mass spectrometry-based metabolomics data. Positions are available starting July 2024, and will remain open until excellent fits are found. Successful candidates will
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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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, and align ultrafast optical setups, integrate setups with measurement electronics for data acquisition, etc. Experience in the following areas is beneficial but not required: nonlinear optics (e.g
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sample of writing in the candidate's field of specialization 4) contact information for three or more references Applications received by November 1, 2025 will be assured of full consideration. Expected
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materials:1) a cover letter of application2) a curriculum vitae3) a sample of writing in the candidate's field of specialization4) contact information for three or more references Applications received by
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closely-related field. Applicants should include a cover letter, a curriculum vitae including a publication list, and contact information for three references by applying on the Princeton University
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, including a list of publications and presentations, a summary of research accomplishments and interests, and the names and contact information of at least three potential references to https
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engineering. We seek candidates with an interest in participating in large multi-PI grants and with collaborative research experiences, experience writing proposals for user-facilities, and/or experience
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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 phylogenomics to work with Professor Tiago Simões. The Simões lab is broadly interested in phylogenetic methods and applications, using morphological and genomic data for reconstructing evolutionary