32 parallel-computing-numerical-methods-"Prof" research jobs at University of California
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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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field. Demonstrated ability to perform research that addresses fundamental science questions. Demonstrated experience with computational chemistry methods including density functional theory, molecular
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on superconducting QPUs. Knowledge of noise and error sources in superconducting systems. Familiarity with benchmarking and characterization methods for quantum computers. Experience with tensor network methods and
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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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An exciting postdoctoral position is available in the exciting field of mathematics of deep learning, under the joint supervision of Prof. Alex Cloninger and Prof. Gal Mishne at UC San Diego. This NSF-funded
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, under the joint supervision of Prof. Alex Cloninger and Prof. Gal Mishne at UC San Diego. This NSF-funded research focuses on a geometric understanding of training in deep neural networks. The position
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. Familiarity with protein analytical methods. Good interpersonal and communication skills to obtain help when needed, such as learning new techniques from collaborators. Skill with molecular cloning. Experience
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Davis comprises four faculty (Profs. Chertok, Citron, Conway, Erbacher), one senior researcher, and a number of postdoctoral researchers and graduate students. The successful candidate will work under the
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UCD Center for Labor and Community - Engaged Research Fellowship Project Coordinator (PROJECT POLICY
accordance with local policies/procedures, and/or enroll in the California Employer Pull Notice Program A MA or terminal degree in Social Science, Law or Humanities and/or equivalent experience/training
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research into control theory of neural population dynamics. This position has the specific focus of developing ML methods to assess the feedback controllability of neural population dynamics recorded from