56 model-checking-"https:" "UCL" "UCL" Postdoctoral positions at Oak Ridge National Laboratory
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
. Focus will largely be in developing and deploying such AI/ML algorithms, closely collaborating with theorists and experimentalists to realize physics- models and/or physics-aware ML-models that can bridge
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and transient inverter modeling and different applications of the simulation. Selection will be based on qualifications, relevant experience, skills, and education. You should be highly self-motivated
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), Energy Science and Technology Directorate (ESTD), at Oak Ridge National Laboratory (ORNL). Major Duties/Responsibilities: Develop physics-based computational models, including Finite Element Analysis (FEA
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Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
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
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Requisition Id 15625 Overview: We are seeking a Postdoctoral Research Associate to advance modeling and AI-driven analysis for magnetic quantum materials, with a focus on neutron scattering and
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for radiation protection and develops many of the biokinetic and dosimetric models recommended by the International Commission on Radiological Protection (ICRP) and applied by U.S. federal agencies
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are especially interested in candidates with strong technical expertise in AI architecture design (e.g., Vision Transformers, foundation models, and federated learning), scalable computing on leadership-class
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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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topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work