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learning, or related quantitative field. • Proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in
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deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in interpretable ML and mechanistic model discovery. Submit a
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will contribute to developing and evaluating state-of-the-art methods for predicting mental health outcomes from multi-modal clinical and digital health data. This position offers the opportunity to work
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, with a particular emphasis on Urban Resilience to Climate Risks. Current research themes include: • Adaptation of People: Leveraging big data and computational methods to analyze adaptation behaviors and
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, doing so in ways that invigorate practices of free expression in the university. Documentary or archival approaches are preferred, but we are open to all methods and humanistic fields of inquiry. All
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. Occupational Summary The David Lab at Duke University (www.ladlab.org ) is recruiting a postdoctoral fellow to join an established research group developing and applying DNA sequencing and computational
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quantitative methods and excited about discovering physical principles of biological organization. Minimum Requirements: PhD in a scientific disciplines, ideally Biology, Bioengineering, Physics or Math
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; (ii) an impact evaluation of a large-scale tree-growing program in Kenya, Tanzania, Uganda, and India; and (iii) an analysis of financial incentives for smallholder tree growing in Ethiopia. In
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. Minimum Requirements: PhD or equivalent doctorate (e.g., ScD, MD, DVM) in psychology, psychiatry, neuroscience, biostatistics, bioinformatics, computer science, or a related field. Research background in
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. The appointment is not part of a clinical training program, unless research training under the supervision of a senior mentor is the primary purpose of the appointment. The Postdoctoral Appointee functions under