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and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful impact? We are currently seeking a/an Part-time University Assistant postdoctoral candidate in
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recognizes that its success is dependent on how well it lives these values, including how we engage, support, and champion members both at DU and in the greater community. To learn more about the University
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/performance trade-offs and typical RAN levers; experience with energy metering data is a plus. • Strong background in AI / Machine Learning for decision-making (e.g., forecasting, optimization with learning
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Qualifications: Experience with development of R packages or other software Minimum 1-year experience in machine learning methods Knowledge, Skills and Abilities: Excellent communication skills (written and oral
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these plants. The post-doc is expected to build upon existing in-house tools and, where applicable, enhance them by means of AI (machine learning) and data-driven methods. These models are aimed to support
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university that provides an accessible, quality education through flexible learning and unparalleled internship opportunities. At UA Little Rock, we prepare our more than 8,900 students to be innovators and
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tools in research. Excellent written and oral communication skills, with a proven track record of publishing scientific papers and delivering presentations. Experience with machine learning techniques
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, and written communication skills evidenced by a publication record in the area of control theory, mathematical optimization, AI, or machine learning. Preferred Qualifications: Publication record in
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
Instructions For information on UNC Postdoctoral Benefits and Services click here Quick Link https://unc.peopleadmin.com/postings/281932 Posting Contact Information Department Contact Name and Title Samantha
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. The focus of this position is developing methods to disentangle dynamic, multiscale ecological signals from large, noisy observational data. This work lies at the interface of statistics, machine learning/AI