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languages Hands-on experience with MD simulation tools such as NAMD, GROMACS, AMBER, or LAMMPS, and visualization tools (e.g., VMD, PyMOL) Experience working on high-performance computing (HPC) systems
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‑guided optimizations across languages (Julia/JACC, Mojo/MLIR, Rust/LLVM). Incorporate Enzyme-based automatic differentiation and multi-language IR tooling for AI‑driven analysis. High‑Productivity
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National Laboratory (ORNL). This is an opportunity to develop novel tools and techniques across scales (river basin to national), publish in top-tier journals, collaborate with industry and academic partners
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and software tools for visualizing and analyzing materials characterization data Develop novel, data-driven materials characterization workflows Advance understanding of process-structure-property
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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: Experience in one more of the following areas: Mathematical tools for data analysis Numerical methods for differential and integral equations Modern machine learning software tools and frameworks
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the last 5 years Preferred Qualifications: Strong background in experimental systems related to heat and mass transfer systems. Knowledge of CFD tools and analytical modelling is preferred. Experience with
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of advanced materials. Research efforts will include the application of density functional theory packages and in-house codes, and the development of supplemental numerical tools, to describe
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and adoption of open standards for scientific dataset processing, including contributing to open-source tools. Mentor interns, students, and peers in cross-domain data readiness approaches. Present
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/device models into open-source software tools for integrated system dynamic and transient simulations. Integrate post-processing measures for simulations to help with automation. Deliver ORNL’s mission by