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the course, and computer-based data analysis is integrated into it. Students will be introduced to the basic principles of statistical computing analytic software. The course will emphasize statistical theory
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information and advice on best-practice methodologies in machine learning/deep learning. It is essential that you hold a PhD/DPhil (or close to completion) in a relevant quantitative field (e.g. biostatistics
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-Computer Interaction (HCI) and related areas. Virginia Tech was founded in 1872 as a land-grant institution and has been long recognized for its tradition of innovation and public service. The IAC is a
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, Nutritional Sciences and Women's Health cluster) for REF was rated as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research
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Tenure-Track Professor in the field of. Sociology with focus on Quantitative Social Science Research
quantitative empirical social research in sociology such as causal inference or machine learning or complex panel data analysis. We are seeking excellent applicants with an international research portfolio and
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the area of end-to-end modular autonomous driving using computer vison and deep learning methods. This includes developing an efficient and interpretable image processing, vision-based perception and
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using SQL, and AI/machine learning models is critical. Faculty members are expected to be exceptional teachers and contribute to the School’s academic environment. Qualifications Successful candidates
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About the Opportunity The Lecturer will teach introductory courses in architectural drawing, sketching, studio design, computer modeling, architectural history, technology, or project case studies
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Rehabilitation Education Counseling PhD programs, (faculty teach four courses per year, which can be reduced through grant buyout or other external funding opportunities). The successful candidate will be able
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combined with human support. About the role The Research Scientist in Machine Learning for Wearables will develop predictive deep learning models to assess maternal and partner health and behaviour