61 model-predicative-control Postdoctoral research jobs at Oak Ridge National Laboratory
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Ridge National Laboratory (ORNL) seeks a motivated Postdoctoral Research Associate. This position primarily focuses on large-scale molecular dynamics (MD) simulations and AI-integrated multiscale modeling
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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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, etc.) to model materials in high temperature or high stress conditions. Experience working with high pressure systems and pressure control systems. Experience setting up data acquisition systems and
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(NSSD). In this role, you will conduct fundamental research into the integration of Bayesian methodologies with system dynamics modeling, advancing statistical methods and the open-source scientific
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for the Postdoctoral Research Associate, Advanced Nuclear Reactor and Fuel Cycle Engineer role. This role is responsible for working with state-of-the-art modeling and simulation capabilities for lattice physics
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Requisition Id 17070 Overview: We are seeking a Postdoctoral Research Associate who will use multiscale modeling and simulation to develop probabilistic lifting frameworks for high-temperature
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Engineer will conduct R&D in nuclear nonproliferation with expertise in computational nuclear reactor physics through modeling and simulation (M&S). The candidate will perform analysis and methods
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common scientific programming languages such as C++, Python, and/or Julia and version control systems. Experience in parallel programming with one or more common parallel programming models, like MPI
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Computing Methods for Physical Sciences Section in CSED. The MsM group is focused on delivering multiscale, multi-fidelity computational models and systems using algorithms and analytics for materials and
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to post-process characterization. This environment enables the creation of high-fidelity digital twins and AI-ready datasets that support real-time monitoring, predictive modeling, and process optimization