69 materials-science research jobs at Lawrence Berkeley National Laboratory in United-State
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of scientists and engineers making significant impacts in stride in the fields of materials science and energy. Join our dynamic team at the The Materials Sciences Division , where we are pioneering the future
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Lawrence Berkeley National Lab’s (LBNL ) has an opening for a Material Sciences Postdoctoral Fellow to join the team. The Center for Non-Perturbative Studies of Functional Materials under Non
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Lawrence Berkeley National Lab’s (LBNL ) has an opening for a Material Sciences Postdoctoral Fellow to join the team. In this exciting role, you will be part of the Non-Equilibrium Magnetic
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Lawrence Berkeley National Lab (LBNL ) is an inclusive and supportive community of scientists and engineers making significant impacts in stride in the fields of materials science and energy. Join
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team environment, including backgrounds in biology, chemistry, earth sciences, statistics, physics, energy technologies, materials science, engineering, and computer science. Analyze and summarize data
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Center and the NERSC scientific computing center, as well as Berkeley Lab's outstanding programs in materials and chemical sciences, among others – offers a prime environment for collaborative science. The
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process design, simulation, scaling science, scenario modeling, and techno economic analysis. The emphasis will be on hydrogen storage materials and hydrogen end uses, gas separation technologies such as
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engineering, mechanical engineering, materials science or related field. Demonstrated experience in structure-property characterization of solid-polymer electrolytes and interfaces, ionomer membranes and thin
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experts to develop data science methods, technologies, and infrastructures to drive scientific breakthroughs. Key focus areas include data modeling and analytics, scalable methods, data lifecycle and
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biosciences, physical sciences, and computational sciences. Your role involves contributing to ACSD’s efforts in understanding biochemical processes for carbon capture by developing AI and inverse design