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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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About the Opportunity This job seeks a postdoc to work on one or more projects related to the development and assessment of wearable robotics. Possible projects topics include: Developing machine
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specifically detailing your interest in glioblastoma and deep learning Two reference letters For more information:stein.aerts@kuleuven.be Where to apply Website https://jobs.vib.be/j/130090/postdoc-position-deep
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-body quantum geometry; altermagnetism; cavity quantum science; quantum non-equilibrium processes; Casimir physics , Non-equilibrium quantum physics , Physics-informed machine learning , Quantum chaos
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astrophysics (completed by the start date), demonstrated experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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partners. The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
care, commitment to the arts and top athletic programs, Carolina is an ideal place to teach, work and learn. One of the best college towns and best places to live in the United States, Chapel Hill has
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develop methods to disentangle dynamic, multiscale ecological signals from large, heterogenous observational data. This work lies at the interface of statistics, machine learning/AI, ecology, and
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learning, computational biology, and AI for science The postdoc will work at the interface of machine learning, genomics, and scientific computing, contributing both methodological innovation and