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In the Computational Materials Group, we focus on the development and use of computational and theoretical methods to understand and predict the behavior of solids, liquids, and nanostructures from
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applying artificial intelligence (AI) and machine learning (ML) methods for the autonomous, self-driving synthesis of nanoscale and quantum materials. This is an exciting opportunity to help shape a new
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catalyst design. This role involves conducting multiscale modeling, spectroscopy simulations, and the development of machine learning methods and automated workflows for multi-fidelity, multiscale, and
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, who develop hardware and software infrastructure for laboratory autonomy, support autonomous laboratories in domains including chemistry, biology, and quantum science, work with domain scientists
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, software systems, simulation capabilities, and experimental workflows for connecting quantum devices with classical computing and networking infrastructure. The successful candidate will work in a
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, who develop hardware and software infrastructure for laboratory autonomy, support autonomous laboratories in domains including chemistry, biology, and quantum science, work with domain scientists
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, vapor transport, flux growth, Bridgman growth, and related methods. Additional desirable qualifications include expertise in crystal structure determination using X-ray diffraction; experience with bulk
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: Bachelors and 5+ years of experience, Masters and 3+ years, or PhD and 0+ years, or equivalent Job Family Research Development (RD) Job Profile Materials/Ceramics/Metallurgical 2 Worker Type Regular Time Type
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statement outlining proposed research directions RD3: Bachelors and 8+ years of experience, Masters and 5+ years, PhD and 4+ years, or equivalent The expected hiring range for this position is $116,250
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(APS). The GL also contributes to the development of next-generation microscopy methods, instrumentation, and data workflows, while overseeing the group’s day-to-day operations, budget, user science