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monitoring in manufacturing environment Develop modular, extensible workflows for data processing Develop and deploy data analytics, machine learning, and statistical modeling methods for multimodal
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researcher to join the Workflow Systems Group and help advance the use of AI in scientific discovery. This position centers on scientific machine learning, automated AI/ML optimization, and high-performance
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Publish scientific results in peer-reviewed journals in a timely manner Participate in project planning and execution Maintain detailed and accurate records Acquire and analyze data in keeping with project
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: To be eligible you must have completed a PhD in chemistry, physics, engineering, or a related field with in the last 5 years. Visa sponsorship is not available for this position. Preferred Qualifications
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. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful
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another, work together, and measure success. Basic Qualifications: A PhD related to computational or theoretical condensed matter physics, theoretical chemistry, theoretical materials science, or other
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD degree in Mechanical
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. Basic Qualifications: A PhD in Materials Science & Engineering, Physics, Chemistry, or a related field completed within the last 5 years A minimum of 2 years of post-Ph.D. experience utilizing
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dark-field STEM imaging, energy dispersive X-ray spectroscopy (EDS) and electron energy loss spectroscopy, at the intersection of electron microscopy, software engineering and machine learning. Major