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experience: Familiarity with machine learning hardware Familiar with high performance scientific infrastructure Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term
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machine learning expertise, with the goal to enhance predictive capability and scalability of multi-scale and multi-physics simulation codes. Perform high-fidelity CFD simulations of complex physical
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electrochemistry. Position Requirements Skill in devising and performing experiments to acquire data; using and maintaining research equipment; compiling, evaluating, and reporting test results. Experience in
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modeling of crystals, dislocation dynamics, and defect analysis, linking atomic-scale simulations to macroscopic properties. Familiarity or interest in machine learning methods and computing frameworks
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in soil processes, plant physiology, plant traits, and species composition Strong data analysis skills, including proficiency in R or Python coding, and/or machine learning techniques Excellent writing
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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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, inclusive, and accessible environment where all can thrive. Additional Preferred Qualifications: Working knowledge of power system protection and control. Familiarity with Machine Learning. Familiarity with
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learning to engineering problems. Experience developing software packages, tools, and data sets for public use. Ability to synthesize effective data visualizations to communicate results from complex data
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researchers performing molecular modeling and machine learning activities. The candidate will be expected to perform ion conductivity experiments with thin film polymer electrolytes. The candidate will use