45 model-predictive-control "https:" Postdoctoral positions at Oak Ridge National Laboratory
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photosynthesis to join the new pilot study of Generative Pretrained Transformer for genomic photosynthesis (GPTgp). The GPTgp project aims to develop a foundational holistic model of photosynthesis that will scale
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relationships between data and metadata. Collaborate on innovative solutions to automate and optimize the interplay between large scientific simulations, data ingestion, and AI processes (e.g., model training
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atomic vibrations control energy transport and functionality and (2) develop an understanding of scattering–elasticity relationships in quantum and magnetic materials. Research thrusts for the position may
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. The goal of this work is to investigate the dynamics of beams with intense space charge and benchmark simulation models against experimental results. As a U.S. Department of Energy (DOE) Office of Science
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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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characterization, and predictive fault tolerance in HPC systems. Architectural exploration and performance modeling of high-bandwidth memory (HBM) and DDR memory systems in the context of data-intensive scientific
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Requisition Id 15663 Overview: We are seeking a Post Doctoral Research Associate who will focus on high fidelity building energy modeling and advanced control. This position resides in
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national security, proliferation detection, and nuclear forensics applications. This position resides in the Collection Science and Engineering Group in the Material Characterization and Modeling Section
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Requisition Id 15094 Overview: Oak Ridge National Laboratory is seeking a Postdoctoral Research Associate who will focus on material property testing, analysis, and modeling of reactor isotope
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thermomechanics. Major Duties/Responsibilities: Help to develop and apply physics-based and/or machine learning models for advanced manufacturing processes. Author peer reviewed papers for journals and conference