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processes. The successful candidate will write publications and make presentations of the results at conferences and meetings. Position Requirements PhD in Aerospace Engineering, Mechanical Engineering
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), widely utilized for characterizing biomacromolecules, polymers and other nano-scale structures. The successful candidate will join an interdisciplinary team and leverage machine learning or artificial
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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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such we are looking for a candidate that has shown the ability to create their own experimental apparatus to solve challenges. The position will require flexibility and a willingness to learn new
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for this exciting opportunity. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in Economics, Regional Science, Public Policy, Engineering, or a related field
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with computer-aided design software. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and analysis of large datasets, and
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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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Argonne National Laboratory invites applications from outstanding candidates with a background in artificial intelligence (AI) and machine learning (ML) for postdoctoral research positions 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