14 plasma-physic-phd Fellowship positions at Lawrence Berkeley National Laboratory in United States
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). Analytical chemistry analysis including inductively coupled plasma mass spectrometry (ICP-MS). Thin film deposition and materials processing including atomic layer deposition (ALD), sputtering and e-beam
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to mitigate any observed bottlenecks by means of materials design or device straggles. Such analyses may include modeling of the polymer properties and various physics occurring in these material systems. You
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chemical and physical principles along with an ability and eagerness to learn and apply new techniques. What You Will Do: Design and fabrication of electrodes and electrochemical devices using techniques
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nuclear structure research within the group. Additional Responsibilities as needed: May mentor PhD or undergraduate students. Other duties as assigned. What is Required: Ph.D. in Nuclear Physics/Chemistry
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of experimental results for publication in peer-reviewed journals. Actively contribute to ongoing efforts in nuclear structure research within the group. Additional Responsibilities as needed: May mentor PhD
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process variables and product outcomes. The candidate should be familiar with electrochemical and mechanical concepts. What You Will Do: Systematically vary mixing conditions to produce battery slurries and
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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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. Publish results in peer reviewed journals and conferences. Additional Responsibilities as needed: Excellent presentation skills. What is Required: PhD degree in chemistry, physics, applied mathematics or a
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developing new funding initiatives as needed. What is Required: Requires a PhD in Physical sciences (physics, chemistry and materials science), Quantum Information, Mathematics, Computer Science, Computational
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electron tomography data. Develop new data-driven methods that leverage physics-informed machine learning for reconstructing non-rigid deformations and generative modeling of conformational heterogeneity in