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encompass: Catalysts Synthesis: Utilize your expertise in materials synthesis to develop novel catalysts guided by machine learning algorithms Catalyst Performance Evaluation: Utilize aqueous electrochemical
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for future Exascale architectures. All candidates are encouraged to apply if they have a genuine interest learning and enhancing these skills. Candidate is looking forward to work and engage with a diverse
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, and safe laboratory practices. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in the field of Chemistry or a closely related discipline Demonstrated expertise in
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knowledge of advances in modeling, simulation, machine learning, and data analysis. Employment of these models on high performance computers and cloud-based infrastructures. Design, develop, and deploy novel
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their environmental impacts including energy use, water use, emissions, and resource depletion. Coursework and some project experience in application of mathematical optimization, statistics, or machine
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of impact, safety, respect, integrity, and teamwork. This level of knowledge is typically achieved through a formal education in materials science, physics or related discipline at the PhD level or
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-be-completed PhD (typically completed within the last 0-5 years) in chemistry, chemical engineering, material science, or related fields. Demonstrated knowledge in materials synthesis and
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intelligence Experience with multimodal knowledge graphs Experience with developing machine-learned interatomic potentials and deploying them in large-scale molecular simulations. Experience with scientific
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Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field(s) of chemistry, material science, or chemical engineering Knowledge of synchrotron-based X-ray characterization methods
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. The candidate will engage in research related to water electrolysis for hydrogen production including materials, performance, durability, components and systems. Position Requirements PhD or equivalent in