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Requisition Id 15885 Overview: We are seeking a Postdoctoral Research Associate – Simulation and Machine Learning for Composite Manufacturing who will focus on developing physics-based simulation
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strengths in any of these areas — quantitative imaging, modeling/transport science, machine learning, or scientific programming — are encouraged to apply. Major Duties/Responsibilities: Lead energy‑storage
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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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competing structural phases and the vibrational and electronic structure in materials with defects and disorder. This effort will further seek to implement strategies to leverage machine learning techniques
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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multiple types of sensing modalities, where this expertise is applied to solve critical national problems in energy and security. Demonstrated knowledge of emerging AI and machine learning techniques as
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte