10 signal-processing-postdoctoral Postdoctoral positions at The University of Memphis
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of Civil, Construction and Environmental Engineering at the University of Memphis invites applications for a postdoctoral fellow position in transportation beginning as soon as possible in the broader area
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of Memphis, has an opening for a postdoctoral position focusing on the development of novel methodologies for prioritizing the location of distributed stormwater control measures aimed at hydrologically
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Department Biology Position Summary The Sutter Laboratory in the Department of Biological Sciences at the University of Memphis is seeking a postdoctoral fellow with strong experience in molecular and cell
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Position Summary The Fogelman College of Business and Economics (FCBE) at the University of Memphis is seeking applicants for a postdoctoral research fellow position in cross-disciplinary business data
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postdoctoral research fellow position in workforce analytics. Candidates are expected to have a program of research in human resources, diversity and inclusion, workforce development, or related areas with
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Department Vice President Research Position Summary Deep Science Postdoctoral R&D Entrepreneurship Fellows will work in a highly collaborative environment to conceive experiments, conduct research, and analyze
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Position Summary The Department of Economics in the Fogelman College of Business and Economics at the University of Memphis is seeking applicants for a postdoctoral research fellow. This position will
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, instructional designs, and supporting tools (including applications of AI) to enhance learner outcomes in the acquisition of beginning-to-advanced computer programming and software development skills. The post
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system, natural language processing, and cognitive science. Specifically, the position is for the newly funded project iCODE: Adaptive Training of Students’ Code Comprehension Processes whose goal is to
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modeling. The work will focus on application of state-of-the-art Data Mining/Natural Language Processing techniques for information from published literature, development of classical machine-learning models