10 atomic-physics research jobs at Lawrence Berkeley National Laboratory in United-States in United States
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contributing to lab testing, physical and virtual demonstration, literature review, engineering design, data analysis activities and more. Working with an interdisciplinary team of researchers and other
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Required: Ph.D., in mechanical engineering, physics, materials science and engineering, within the last 3 years. Specialization in nano-scale thermal transport preferred. Knowledge and experience in modeling
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: Ph.D. in Physics or Electrical Engineering. Experience in optics, UHV systems, laser stabilization, and system assembly. Background in cavity QED, in particular, coupling atoms with cavity fields
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. The employee will be a key member of the team and will report to the team lead. The purpose of this position is to conduct complex applied research, demonstration and deployment related to improving
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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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materials characterization including scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoemission spectroscopy (XPS), X-ray fluorescence (XRF), and atomic force microscopy (AFM
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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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and will report to the team lead. The purpose of this position is to conduct complex applied research, demonstration and deployment related to improving the operational performance of commercial
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at the Molecular Foundry to provide atomic-level insights linking the structure and performance of materials and electrochemical devices enabling performance optimization of photoelectrochemical systems for CO2
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fluid flow; (3) models for different sources of noise in the geophysical data. What You Will Do: Development of physics-informed ML models for seismic data interpretation and upscaling of petrophysical