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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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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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vision and novel applications of machine learning. Advanced knowledge of R or Python is required. Intermediate knowledge in C/C++ and/or at least one SQL dialect is preferred. Apply online at https
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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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techniques; large data processing and high performance computing; advanced causal inference and statistics; computer vision and novel applications of machine learning. Advanced knowledge of R or Python is
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biophysics -experimental and/or computational genomics -computer science, statistics, and/or machine learning with applications relevant to genomics -bioinformatics -population genetics / genomics
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and machine learning with Prof. Jason M. Klusowski (https://klusowski.princeton.edu). The position is for one year with the possibility of reappointment based on satisfactory performance and
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engagement, enabling distinctive impact, both academic and societal. In this context, we welcome applicants from diverse backgrounds such as computer science, electrical and computer engineering, sociology