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scalability studies to identify and improve bottlenecks in large codes. Experience in development of data-driven reduced-order models in one or more of these areas: turbulence, boundary layer flows, combustion
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This position focuses on the research and development of novel radiation detectors and associated edge-computing circuits and algorithms for X-ray, particle, and nuclear physics experiments
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on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density
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development and web-based applications, back-end services and API design (e.g., FastAPI, Flask), and deploying applications in local or cloud environments. Experience working with large-scale datasets
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tool development. Application Procedure: Interested candidates should submit: A detailed CV A cover letter describing your research experience and interests. GitHub profile (if available) Job Family
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contribute to research and model development to enhance the resilience of domestic and global supply chains for clean energy technologies. Lead technical and policy analysis to inform decision-makers
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design, development of supervisory control logic, validation of vehicle models against test data etc. The projects and interests of this group span all modes of transportation including off-road, rail
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. Experience in numerical methods and CFD development using mesh-based scientific codes. Expertise in the lattice Boltzmann method (LBM) as evidenced by their publications High performance computing (HPC
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, machine learning, and control in the energy sector. The postdoc researcher will perform theoretical study and algorithm development on optimization/control/data analytics methods and authorize peer-reviewed
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have a strong background in fundamental electrochemistry, with preferable hands-on expertise in computational materials science. The applicant should be well versed in code development, application of AI