10 electrical-engineering-coding-theory Fellowship positions at University of California
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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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. Proven ability to bridge theory and experiment, particularly in the context of spectroscopy or operando studies. Excellent communication and software engineering practices, including proficiency with Git
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on zoonotic virus reservoir species. Experience creating research summaries, updates, and reports. Excellent written and communications skills. Familiarity with specific technology including but not limited
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, materials science, engineering, or a related discipline, with no more than three years of prior postdoctoral experience. Strong academic track record shown through publications and scientific presentations
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work in the Energy Storage and Distributed Resources Division. You will support the research programs in the Electrochemistry Group aiming to develop next-generation batteries for use in electric
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Qualifications: Experience in one or more of the following areas: quantum sensing, theory development aimed at experimental searches for new physics, and foundational knowledge of particle physics. The posting
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, speak, and understand the English language. Ability to utilize medical terminology appropriately. Knowledge of current trends in nursing. Commitment to excellence in patient care. Effective interpersonal
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UCD Center for Labor and Community - Engaged Research Fellowship Project Coordinator (PROJECT POLICY
: Monthly Salary Grade: Grade 21 UC Job Title: PROJECT POLICY ANL 3 UC Job Code: 007398 Number of Positions: 1 Appointment Type: Staff: Contract Percentage of Time: 100% Shift (Work Schedule): Monday - Friday
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join the Workflow Readiness team as part of NERSC’s Exascale Science Acceleration Program (NESAP ). You’ll work with NERSC staff, domain scientists, and engineers from industry partners to prepare key
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or experience in distributed training on large scientific datasets and staying current with new training methods and architectures. Experience with performance and profiling tools such as Perftools, NVIDIA Nsight