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quantification. You will research and develop frontier analysis methods, including machine-learning approaches, for the accelerated interpretation of x-ray scattering data. You will develop multi-modal data
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, operation, control, and security. Required Knowledge, Skills, and Abilities: A PhD. with a broad knowledge of electric power systems. Demonstrated background in artificial intelligence (AI)/machine learning
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planning, military service, illness or other life-changing events. Candidates must have received a PhD. in physics, materials science, or a related field within the last five years or will have completed all
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finishing high-curvature X-ray mirrors. o Enhance the determinism (predictability) of the IBF process by leveraging artificial intelligence or machine learning (AI/ML). • Publish results in peer-reviewed
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signal processing and analysis Experience in applying machine learning to scientific problems Experience in heterogeneous computing (e.g. GPUs) OTHER INFORMATION: Please submit your resume/CV, research
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publications or software) • Knowledge of machine-learning algorithms. OTHER INFORMATION: BNL policy requires that after obtaining a PhD, eligible candidates for research associate appointments may not exceed
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techniques. Position 2 - Electronic structure simulation of complex electrocatalysts (metal/metal compound), kinetic modeling of electrochemical performances under reaction conditions and machine-learning
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of electrical power systems. Perform research on power system dynamics and control under high inverter-based resources (IBRs). Develop and apply artificial intelligence (AI)/machine learning (ML) techniques
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intelligence (AI) and machine learning (ML) techniques to aid in developing protocols and separations.This position has a high level of interaction with an international and multicultural scientific community
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events The initial two-year term appointment is subject to renewal contingent on performance and funding Preferred Knowledge, Skills, and Abilities: Knowledge in one or more of: Scientific machine learning