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operating experimental systems for separation processes; conducting laboratory-scale studies involving radiotracers and actinide/lanthanide separations; supporting the development and optimization of aqueous
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interdisciplinary team, the candidate will work closely with both the Q-NEXT Argonne Quantum Foundry along with the Quantum Metamaterials group within the Materials Science Division at Argonne National Laboratory
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energy supply systems, multi-objective and stochastic optimization, advanced statistical analysis, and data visualization. This position offers the opportunity to work with a multidisciplinary team of
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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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. 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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. The candidate will work closely with computational modeling collaborators to validate reactor designs and optimize operating parameters. The candidate will be expected to contribute to report preparation
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, composite processing, and manufacturing science to establish structure-processing-property relationships that enable high-performance multifunctional materials. The candidate will work closely with scientists
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learning, or optimization Strong programming skills in Python and experience with scientific computing and machine-learning libraries Ability to work across experimental, robotic, and computational systems
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for accelerators, beamlines, insertion devices, or large-scale user facilities. Practical application of AI/ML techniques to controls, diagnostics, or operational optimization. Familiarity with distributed control
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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