64 parallel-computing-numerical-methods-"Prof" Postdoctoral positions at Duke University
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, evidence-based practice, and interdisciplinary collaboration to lead the future of nursing. Our nursing program stands as a leader in healthcare education, earning #1 in the nation for Best Bachelor
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at https://energyaccess.duke.edu/ . The scholar selected for this position will work closely with EAP faculty (including Profs. Marc Jeuland and Robyn Meeks) and with co-director Jonathan Phillips, as
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quantum optics to support and advance quantum science research. The position involves theoretical modeling of quantum optical and many-body systems, numerical computation using advanced integration and
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in cosmology. The candidate will work with Prof. Arun Kannawadi and Prof. Michael Troxel on building next-generation pipelines for joint analyses of imaging data from the Rubin Observatory’s LSST and
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program in ML / AI at the speed of wireless in Dr. Robert Calderbank’s research group. The successful candidate will conduct numerical simulations, develop and implement computational methods to interpret
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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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, and who have strong grounding in the methods and practice of oral history. The holder of this postdoc will teach two courses a year, one an introduction to oral history method and practice, and one
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. Robert Calderbank’s research group. The successful candidate will conduct numerical simulations, develop and implement computational methods to interpret complex data, and contribute to the preparation
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, numerical methods, and Earth system modeling to develop and evaluate a coupled xylem–phloem transport framework that translates multiscale physics into next-generation vegetation model schemes. Key
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the global health scenario and domestically for dissemination, and plenty of opportunities for career advancement. •Learn background/research methods of studies for which analysis is conducted with limited