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, to identify and characterize novel transcriptional regulatory mechanisms that control traits of agronomic importance. This project thus offers a unique opportunity to train a future leader in combining
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is a transdisciplinary collaboration of over 30 investigators from the colleges of Agriculture, Engineering, Liberal Arts, Libraries, Polytechnic and Science that integrates expertise in AI/ML
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‘best-practices’ in semiconductor processing. Qualifications A successful candidate should have: M.S. or PhD in Physics, Electrical Engineering, Materials Science, or related Engineering discipline
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, and past experience at publishing in technical journals. Education Ph.D. in Mechanical, Chemical, Materials Engineering or related field. Experience Applicants should have extensive experience in