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-based (in planta) expression systems. Membrane proteins production and purification Strong molecular biology skills (cloning, vector design, transformation), protein purification and analysis, and
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for the conceptual framework, design, and implementation of these machine learning models, ensuring trustworthy computations and scalability on the DOE’s leadership computing facilities. The focus will be
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physics knowledge into DL model design and training, these models outperform traditional methods even without labeled training data (https://www.nature.com/articles/s41524-022-00803-w ). Application spaces
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-house codes and making use of high-performance computing (HPC) tools. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical, aerospace
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The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing
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of Energy projects. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in Electrical Engineering. Onsite presence is required. Solid foundation in
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policy, environmental science, or a related field at the PhD level with zero to five years of employment experience. Technical background in economics with a focus on the mineral and energy sectors. Proven
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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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PhD (typically completed within the last 0-5 years) in pyrometallurgy, chemistry, materials science, chemical engineering, or related scientific background with 0-3 years’ experience. Experience in
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(predoctoral) or PhD (postdoctoral) in Materials Science, Chemistry, Physics, or related area is required. Coursework in computer science or data science is desirable. Familiarity with research data management