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their field of study Engage with new areas of basic and applied research Transition classroom theory into hands-on experience Network with world-class scientists Exchange ideas and skills with the Laboratory community
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their field of study Engage with new areas of basic and applied research Transition classroom theory into hands-on experience Network with world-class scientists Exchange ideas and skills with the Laboratory community
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 2 months ago
their field of study Engage with new areas of basic and applied research Transition classroom theory into hands-on experience Network with world-class scientists Exchange ideas and skills with the Laboratory community
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 1 hour ago
their field of study Engage with new areas of basic and applied research Transition classroom theory into hands-on experience Network with world-class scientists Exchange ideas and skills with the Laboratory community
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 1 month ago
their field of study Engage with new areas of basic and applied research Transition classroom theory into hands-on experience Network with world-class scientists Exchange ideas and skills with the Laboratory community
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develop the data-driven foresight methods to find out. About us At the Division of Physical Resource Theory, we study how societies can transition towards sustainability. Building on physics and systems
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, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization
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of functional programming and formal methods. In addition to theory development, your work will have the potential to scale algebraically-accountable architectures to high-stakes, continuous domains, encompassing
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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