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full-time (37 hours) contract, in 10 months. The candidate will conduct cutting-edge research in Human-Computer Interaction, with a focus on novel interactive technologies, including conversational
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PhD (or equivalent) in Machine Learning, Computer Science/Engineering, Biomedical Engineering, or PhD or equivalent degree in population/public health and other related medical fields with AI and ML
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» Chemical engineering Engineering » Process engineering Engineering » Industrial engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 4 Feb 2026 - 23:59
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that support the unit for area protection and marine spatial planning, as well as operations at SLU Aqua. Your profile You have documented expertise in marine ecology and computer vision and machine learning
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benefits and perks (available for benefits eligible positions only ): Health: Medical, Dental and Vision plans available for qualifying staff and family Holiday, Vacation and Sick Leave Education discount
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
, as articulated in the School’s Strategic Plan (https://pharmacy.unc.edu/about/oe/strategic-plan/). Our Vision is to be the global leader in pharmacy and pharmaceutical sciences. Our Mission is to
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collaboration activities. Experience with teaching and independently supervising project courses, and BSc and MSc thesis projects. Preferably, the candidates have experience with: Medical robotics Computer vision
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. This posting is to fill an existing vacancy within the University. Qualifications Candidates must have a Bachelor’s degree and a PhD in Electrical and Computer Engineering or equivalent degrees completed
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of scholars and students at the forefront of computer vision research and material studies with longstanding research strengths in architectural history. The Center for Virtual and Material Studies (https
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experimental design. Proficiency with machine vision and deep learning techniques, including image segmentation, landmark placement and metric learning, for the automation of phenotypic analysis of large image