36 Law "Mathematics" positions at Lawrence Berkeley National Laboratory in United-States
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conferences. Required Qualifications: Ph.D. in Computer Science, Computational Science, Applied Mathematics or an equivalent/related field awarded within the last five years. Experience programming in one
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Berkeley Lab’s (LBNL ) Molecular Biophysics and Integrated Bioimaging Division (MBIB ) has an opening for a Postdoctoral Fellow to jointly work with the Applied Mathematics and Computational
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properties of isolated battery and related energy materials with nanometer scale spatial resolution. Support the PI to help develop accurate mathematical models of the thermal properties of the battery systems
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skills, knowledge, and abilities, including education, certifications, and years of experience. Requirements: Employer will accept a Bachelor's degree in Electrical Engineering, Mathematics, Physics
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Bachelor's degree in Electrical Engineering, Mathematics, Physics, or related field and 4 years of experience in the job offered or in an electrical engineering-related occupation. Position requires: 1. AC
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Mathematics and Computational Research Division and Scientific Data Division. Operating in a dynamic setting with varied priorities, you'll need strong judgment and initiative to foresee and solve problems
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, and materials necessary to accomplish assignments. Access database information to identify, select and procure parts, components, and materials. Perform complex mathematical calculations. Mechanical
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and procure parts, components, and materials. Perform complex mathematical calculations. Mechanical Engineering Technician (Level 1) Requirements: 1-2 years of experience in a mechanical, electro
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or machine learning based controls in real buildings. Relevant experience developing mathematical models for building energy simulation. Demonstrated strong skills in developing models in languages and
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/frameworks (e.g., TensorFlow, PyTorch). Proficiency in field-programmable gate array (FPGA) programming in RTL design and verification. Strong background in ML, statistics, and mathematics, with practical