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to provide engaged learning experiences in the classroom. This non-tenure track position is teaching-focused and carries a teaching load of five courses per semester. Candidates with at least an earned
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, Education, Knowledge & Skills PhD in Electrical or Computer Engineering, or closely related field. Record of quality peer-reviewed and other scholarly publications. Demonstrated potential for developing a
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electromechanical devices, or the ability to acquire such skills rapidly on the job. Preferred Qualifications MS in biomedical engineering or related engineering discipline. PhD additionally preferred. Some teaching
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broad range of research areas. We are interested in an experimentalist who can develop large datasets in support of emerging artificial intelligence and machine learning driven advances in fluid dynamics
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reasoning for mathematics (e.g., SAT/SMT solvers, first-order theorem provers) Machine learning for mathematics (e.g., model architectures for theorem proving, data-driven exploration of mathematical
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position in computational soft materials engineering. We seek applicants pursuing research in the area of polymeric and soft matter. We especially welcome applicants with a focus on computational and machine
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courses involved may vary from semester to semester and openings to teach these courses develop on a periodic basis, depending on department teaching needs. These appointments will be part-time and with
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into account all of the benefits that you are eligible for, including health insurance, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please
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Engineering, so we are particularly interested in applicants who apply artificial intelligence / machine learning in their work on large energy systems. Areas of interest include electrification in industry
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environmental engineering (candidate status) and a demonstrated record as a lead author on publication(s) using machine learning algorithms to forecast water quality. Fluency with open source coding languages (R