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Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
part of our research team, you will be working with a highly interdisciplinary team of scientists at the CNMS, and across other divisions at ORNL. Major Duties/Responsibilities: Work closely with members
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large-scale training and post-training pipelines (including distributed data/compute and evaluation harnesses). Collaborate with domain scientists and external partners; co-develop end-to-end AI workflows
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methods towards improving our understanding of unique target materials. You will be working with scientists, engineers, technicians, and safety and quality assurance staff to support material testing and
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, diverse, and multi-institutional team, including experimentalists, design engineers, and computational scientists, on the design and optimization of tritium breeding blankets and fusion reactor cooling
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, transportation, and more, with a special emphasis on grid resilience assessments and equity analysis. You will have the opportunity to creatively use interdisciplinary methods from computational data science
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will involve designing beam dynamics experiments, measurement, simulation, and data analysis. This position resides in the Accelerator Physics Group in the Accelerator Science and Technology Section
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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framework for driven and open quantum systems. Phenomenological modeling of dynamics/transport behaviors in complex systems, including strongly correlated electron systems. Experience in analyzing data from
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research spanning detector simulation, Spiking Neural Network (SNN) design, neuromorphic hardware, and data-rich experimental systems such as CMS pixel detectors, Timepix4, and novel photodetector
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capabilities for advanced nuclear materials systems. In addition, this work includes developing processes that connect mechanical and thermophysical testing data with the microstructures of ceramic and metallic