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The National Energy Research Scientific Computing Center (NERSC ) at Berkeley Lab seeks a highly motivated Postdoctoral Researcher — Scientific Machine Learning (NESAP) to join the Workflow
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brain organoids. This position requires a high-degree of team work and interdisciplinary activities. What You Will Do: Perform research in machine learning methods based on control theory applied
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techniques, or tight-binding approaches. Proficiency with major simulation packages such as ASE, Quantum ESPRESSO, VASP, CP2K, or LAMMPS, and their Python interfaces. Working knowledge of machine learning
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experience. Knowledge of machine learning. Notes: Appointment Type: This is a full-time, 2-year, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing
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networks) for simulating quantum computer performance. Relevant publication record and clear, effective written/oral communication of scientific results. Knowledge of quantum information science relevant
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research focuses on a geometric understanding of training in deep neural networks. The position offers excellent training opportunities at the intersection of machine learning and applied mathematics
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, machine learning or AI to computational modeling, simulations, and advanced data analytics for scientific discovery in materials science, biology, astronomy, environmental science, energy, particle physics
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University of California, Berkeley, Department of Electrical Engineering and Computer Sciences Position ID: University of California, Berkeley -Department of Electrical Engineering and Computer
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UCD Center for Labor and Community - Engaged Research Fellowship Project Coordinator (PROJECT POLICY
requirements, and changing priorities. Skills to prepare data presentation and marketing materials based on intended audience (colleagues, faculty, students, employers, etc.). Advanced computer skills in
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offers excellent training opportunities at the intersection of machine learning and applied mathematics. Qualifications: - Applicants should have (or expected to get in a near future) a Ph.D. in applied