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capabilities in evaluating physical and chemical properties of environmental samples including plants and soil. Experience integrating phenotypic or imaging-based datasets (e.g., RGB, chlorophyll fluorescence
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to ORNL's Research Code of Conduct. Our full code of conduct, and a statement by the Lab Director's office can be found here: https://www.ornl.gov/content/research-integrity Benefits at ORNL: UT Battelle
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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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represents expertise in nuclear materials synthesis and analysis, deployment of advanced analytical methods including crystallography, imaging, spectroscopy, physical property analysis, neutron scattering, and
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backed by demonstrated recent and relevant research and development in the field is a fundamental requirement of this position. Experience with neutron transport and reactor analysis software (SCALE code
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distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross‑Paradigm Integration(new optional emphasis): Explore how
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Postdoctoral Research Associate - Multifunctional Equipment Integration Energy Conversion Technology
research assignments related to energy conversion systems including: Prototype development Material synthesis and analysis Performance testing Analysis of results Preparing research publications Prepare
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understanding of parallel application development techniques (parallel programming models, algorithms, and software) Preferred Qualifications: Experience in implementing ab initio simulation codes such as VASP
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commercial (e.g. Abaqus, ANSYS, etc.) and/or open-source finite element (FE) codes (e.g., MOOSE, DAMASK, etc.) is required. Experience with microstructural modeling (e.g. crystal plasticity) applied to fatigue
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., neutron imaging, SPRUCE hydraulic/thermal thresholds and scaling Produce and publish AI-ready datasets to the ESS-DIVE data archive and BER data lakehouse Develop AI pipelines for experimental ecophysiology