15 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions at University of California
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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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. Knowledge of performance improvement and evidence-based practice. Basic computer skills. Ability to assess, plan, implement and evaluate patient care, taking into consideration protective interventions
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computer chips. Your role will be to design processes for transforming resources into sustainable materials by modeling reaction pathways from first principles. The project will collaborate with a broad
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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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, to enable scalable quantum algorithm development and quantum-HPC codesign. What is Required: PhD in Computer Science, Computational Science, Applied Mathematics, or a related field awarded within the last
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to field research. Experience with machine learning (ML) approaches to analyzing data. experience in student leadership or mentorship roles. Experience supporting the design and implementation of field
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Generation Nonproliferation Leadership Development Program (NextGen). What You Will Do: Define project scope, objectives, and research strategies for an independent research project advancing nuclear
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electrical engineering, computer science, physics, mechanical engineering, applied math, theoretical neuroscience, or statistics. In depth experience with control theory and machine learning for analysis
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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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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