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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | 19 days ago
the computational work. Researchers in the group have experience with crystal plasticity using finite element and fast Fourier transform methods, fatigue indicator parameters, process modeling and machine learning
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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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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the
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through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance
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enrichment, network analysis, and integration with transcriptomic datasets. Analyzes ATAC-seq and ChIP-seq data, including quality control, peak identification, motif analysis, transcription-factor activity
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Strong background in biomechanics, biomedical engineering, computational mechanics, or equivalent fields. Strong foundation in numerical methods, especially the finite element method. Proficient in python
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Qualifications Experience with analysis of three-dimensional velocity fields Computer programming/scripting experience Experience with graph analysis and optimization methods Required License/Registration
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curious to deliver work that matters, your journey starts here! The Department of Electrical and Computer Engineering (ECE) ranks among the best in the country. Our research programs are at the forefront
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the computational task: first-principles active-site descriptor models, high-throughput screening with machine-learned interatomic potentials, and a dedicated catalysis database. This position is ideal for a