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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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an air force base involving UAV activities Visit to a navy base Battlefield walk ("staff ride" learning experience) Visit to the security fence and a checkpoint to learn operations/procedures Visit
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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postdoctoral or more senior researcher interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling
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, faculty, and staff in Computer Science, Electrical and Computer Engineering, Princeton Language and Intelligence, the Princeton Center for Statistics and Machine Learning, and the greater STEM community
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limited to synthetic and chemical biology approaches to cellular computation and biomolecular logic design, the development and implementation of novel algorithms, machine learning for parsing biological
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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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references is required. To learn more about AI at Princeton, please visit https://ai.princeton.edu. Princeton University is committed to fostering a diverse and inclusive academic community. To maximize
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Language and Intelligence, the Princeton Center for Statistics and Machine Learning, and the greater STEM community. This position works closely with librarians and information specialists, guiding
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for three references is required. To learn more about CITP's fellows' program please visit our website (https://citp.princeton.edu/programs/fellows/). CITP is committed to building a culturally diverse and