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Lexington, KY Grade Level 05 Salary Range $15.00-22.60/hour Type of Position Staff Position Time Status Full-Time Required Education HS Click here for more information about equivalencies: https://hr.uky.edu
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programs Posting Summary The School of Nursing is currently seeking part time faculty to teach in our Division of Advanced Nursing Practice during the upcoming semester in the clinical and/or didactic
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undergraduates in scientific research projects. ESSENTIAL DUTIES & RESPONSIBILITIES INCLUDE: 1) Computer simulations of protein structure and computational protein design of small peptides and proteins. 2
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policies, practices or procedures. Apply online at https://pointloma.wd1.myworkdayjobs.com/en-US/PLNUCareers/job/Mission-Valley-Campus---San-Diego-CA/Director-of-Military-Student-Engagement-and-Strategic
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). Applying advanced statistical and machine learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development
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statistics, and statistical programming packages. May utilize artificial intelligence and/or machine learning techniques/methods. Data Management Focuses on ensuring the efficient and secure handling of data
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materials according to the Lambert–Beer law, thus enabling an accurate description of PEC device behavior. In parallel, the coupling between kMC and CFD simulations will be achieved through machine learning
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, early detection of degradation, and residual life prediction. The program integrates physical modeling, machine learning, and data fusion techniques to optimize predictive maintenance, reduce operating
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to teach in various topics within the discipline, including German, French, and Spanish. Duties typically involve teaching elementary-level language courses, although Lecturers will sometimes also teach more
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proposals. Responsibilities Develop, implement, and evaluate new statistical and machine learning methods aligned with the two themes above. Lead and co-author manuscripts in statistical, machine learning