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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 11 hours ago
Organization National Energy Technology Laboratory (NETL) Reference Code NETL-PIP-2026-Lackey How to Apply A complete application consists of: An application, including academic history, work
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to learn about, (road) network traffic flow theory and simulation. You are interested in mentoring and supporting MSc and PhD students. You are a machine learning enthusiast (and realist). You love coding
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for research. Research experience in wireless communications, signal processing, information theory, or related areas. Preferred Qualifications: Research experience in machine learning for wireless
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and machine learning with density functional theory, or other similarly relevant computational methods, to advance understanding of materials design predictions for 2D and 3D systems with electronic and
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. Experience with computational solid mechanics, computational soft tissue mechanics, or a closely related mechanics field. Well-developed coding expertise in Python, Julia, C++, Fortran, or a similar scientific
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motivation to enhance our multidisciplinary research at the intersection of control theory and machine intelligence. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control
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swimming. The successful candidate will lead quantitative analysis of animal configuration and related flow field topology utilizing graph theory to assess the impact of swimming and environmental conditions
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-interaction treatment of SOC, while scalar relativistic effects will be included via the spin-free exact two-component theory in its one-electron variant (SFX2C-1e), thereby extending the scope of EOM-CC
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milestone-driven sponsored research projects and communicating effectively with stakeholders Training and experience with two-phase flow and heat transfer with refrigerants, theory and experiments, is
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, ensuring the agent's responses are pedagogically valid, contextually appropriate, and consistent with collaborative learning theory. Scaffolding mechanism design. In close collaboration with the PhD