74 scholarship-phd-agent-based-modelling Postdoctoral positions at Oak Ridge National Laboratory
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, qualification, and deployment of AI agents and models, Computational Fluid Dynamics (CFD) simulation codes, and Finite Element Method (FEM) based tools for nuclear energy (fission and fusion) applications
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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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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be expected to model prototypes of the fiber optic based sensors using standard Multiphysics software, perform out-of pile laboratory testing of the instruments in representative environments, and
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AI processes (e.g., model training, inference). Develop agentic AI systems and AI harnessing techniques to enhance model quality, resource optimization, and adaptive execution in diverse workflows
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guided by AI/ML, remote sensing, process-based modeling, field observations and experiments, and advanced analytical techniques. Major Duties/Responsibilities: Independently and collaboratively lead field
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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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the computational foundations of probabilistic programming, such as automatic differentiation, tensor libraries (PyTensor, JAX), gradient-based samplers, or model transpilation and compilation. Experience with
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models of gas transport and process behavior in industrial systems Collaborate with a team of scientists from across the national laboratory complex on modeling efforts Extend process flow modeling across
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such as quantum and analog computational models. You will explore how compilers, runtimes, and AI-driven agents can co-optimize complex architectures, reasoning across conventional processors (CPUs/GPUs