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, the College of Design, and the School of Journalism and Communication. The department supports a computer lab and an equipment room in the Knight Library. Position Summary The Cinema Studies Department is
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Computer Science, MD degree, or related field. Experience and Skills: Experience in image or signal processing, medical data processing, artificial intelligence and/or computer vision. Basic system administration
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Computer Science, MD degree, or related field. Experience and Skills: Experience in image or signal processing, medical data processing, artificial intelligence and/or computer vision. Basic system administration
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that developments are well understood and embedded. To apply electrical/electronic design skills to create a prototype including circuit analysis, computer aided design and complex simulation tools, e.g. MATLAB/SPICE
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strengths of the University of Tübingen in Computer Sciences and Machine Learning. Potential research directions include, but are not limited to, phylogenetic, demographic, ecological and biogeographic
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skills: MS Windows, Word, Excel, Internet, e-mail, e-business LanguagesENGLISHLevelExcellent Additional Information Work Location(s) Number of offers available1Company/InstituteUniversity of Maribor
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information at https://www.cs.pitt.edu/ . About the Positions Appointment Stream (Teaching Assistant Professor, Department of Computer Science, #27041) Appointment Stream (Teaching Assistant Professor
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and expertise with the following software: Microsoft Office Suite, specifically Microsoft Word, Microsoft Excel, PowerPoint, Publisher and Outlook. Physical Demands and Work Environment Content will
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hours in History is required. • PhD preferred. Minimum Experience/Training: • Prior college teaching experience is preferred. • The successful candidate must have a high level of computer literacy and a
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. Qualifications: Strong research productivity and demonstrated potentials; Ph.D. (Electrical Engineering, Computer Engineering, Computer Science, Statistics, Applied Math, or related field); A minimum relevant