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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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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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, 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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. In this role, you will lead a research program centered on AI-driven autonomous synthesis, including: Active learning and Bayesian optimization over synthesis parameters such as precursors, temperature
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PhD (within the last 0-5 years) in field of materials science, chemistry, chemical engineering, computer science, or a related field Experience operating and troubleshooting laboratory automation
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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
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device-relevant properties Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and improve data efficiency in
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pressure-temperature measurements with advanced x-ray platforms. This includes working in optimizing applicaton of APS-Upgraded high brightness and high coherence beam across APS and at HPCAT and
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and optimize sulfur cathode materials and related host structures Perform physical, chemical, and structural characterization of battery material Fabricate and evaluate lithium–sulfur cells in coin-cell
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evaluate well-defined nanomaterials for catalytic and electrochemical applications Develop, implement, and optimize in situ electrochemical cells to probe liquid–solid interfaces under reaction conditions