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the application of statistics and machine learning in social science. The position requires no teaching, though teaching opportunities may be provided if requested. When teaching, successful candidates will carry
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, facilitating impactful multidisciplinary collaboration, scholarly advancement, and the creation of tools and public goods. Requirements Applicants must have (or expect to have at time of appointment) a PhD in
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or another life-science field A demonstrated ability to use state-of-the-art instruments to address a scientific question A demonstrated ability to secure external funding (e.g., travel grants, PhD fellowships
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for Energy, Environment and Sustainability *Optical Materials and Light Matter Interactions *Quantum Materials Science Qualifications A PhD in Materials Science, Optics, Physics, Chemistry, Electrical
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stakeholders involved in planning and decision-making. Qualifications Candidates must have a PhD in atmospheric physics, hydrology, meteorology, Earth system science, climate studies, applied mathematics
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spectrometric datasets. A major focus will be on the application of AI/machine learning models and other computational methods to discover unknown metabolites that have strong associations to experimental
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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