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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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. Experience with Bayesian methods, graph/network analytics, reinforcement learning, or other advanced AI approaches relevant to industrial systems. Experience with geospatial analysis, spatial data integration
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model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships, and are familiar with optimization approaches such as genetic search, Bayesian
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of transportation including off-road, rail & marine applications too. These projects involve close collaboration with industrial and academic partners, and engagement with multidisciplinary project teams
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field at the Ph.D. level with zero to five years of employment experience. Demonstrated experience in leading research initiatives, with a strong track record of publishing in peer-reviewed journals
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). Skilled oral and written communication skills. Proven track record showing the ability to carry out independent and collaborative research in a multidisciplinary team while meeting project deliverables and
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scientific problem solving as evidenced by publications and recommendations. Proven track record showing the ability to carry out independent and collaborative research in a multidisciplinary team while
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. Experience with MLOps, reproducible workflows, experiment tracking, CI/CD, software testing, benchmarking, or open-source software development. Familiarity with biomedical AI validation, data readiness
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considered). A proven track record demonstrated through journal publications and patent applications. Skilled communication skills at all levels of the organization. Ability to work in a team environment
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implementing the scientific research, and for reporting relevant results in an appropriate form. The successful candidate should be highly motivated for research excellence and have a strong track record in