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systems Excellent written and oral communication skills Experience with time-dependent density functional theory (TDDFT) and excited-state optimization, which is considered an asset Ability to model
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Qualifications: Experience using the MOOSE simulation framework is highly desired. Experience fitting complicated physics-based models against test data, including machine learning and Bayesian optimization
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AI methods and multimodal data training approaches, fine tuning and optimizing inference is desired. Background in atmospheric science, especially weather modeling, are particularly sought after
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materials. The focus is to develop novel electrochemical deposition methods, control interfacial reaction kinetics, and engineer functional coatings and thin films. Advanced process design and optimization
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research in radiochemical separations and advanced aqueous recycling of used nuclear fuel (UNF). The successful candidate will contribute to the development and optimization of advanced aqueous recycling
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applied research on AI-driven and AI-enhanced industrial energy systems optimization modeling, material flow analysis, and supply chain analysis of industrial commodities and critical materials
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. Your responsibilities will encompass pioneering novel synthesis doping techniques for "quantum grade" diamond, optimizing surface termination methods, and developing deterministic synthesis of pertinent
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, characterizing mass transfer and selectivity under flow conditions, and screening and tuning DES compositions to optimize solubility, speciation, and electrochemical accessibility for target elements
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breakthroughs in NV sensor synthesis and host diamond heterointegration. The successful candidate will operate at the interface of these programs, playing a central role in developing and optimizing next
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, and related materials. Research activities will include developing solution-based processing methods, optimizing powder mixing and dispersion, fabricating dense composites through thermal consolidation