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computational simulations in the area of density functional theory (DFT). The role will focus on understanding and predicting the properties of novel functional materials, including surfaces and interfaces
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. Ideal candidate will bring a robust background in nuclear theory, with experience in areas such as Density Functional Theory, the nuclear Shell Model, or Effective Field Theory. Learn more about the
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chemical methods (beyond density functional theory); experience in theory development and applications to heterogeneous catalysis and complex reactive interfaces involving metal nanoclusters are essential
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Lawrence Berkeley National Lab’s (LBNL ) Energy Storage & Distributed Resources Division has an opening for a Postdoctoral Fellow to join the team. In this exciting role, you will conduct research
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density functional theory (DFT) methods integrated with data-driven artificial intelligence techniques. Research may encompass the development and application of machine learning force fields, high
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Computational Chemistry, Catalysis, Energy Chemistry, or a related field. Extensive expertise in Density Functional Theory (DFT) for material design and optimization. Proficiency in computational tools and