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and expanding team. You’ll play a key role in our success through your code, publications, and strategic promotion of our work. * PhD in Computer Science, Biomedical Informatics, Machine Learning
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Requirements: A Ph.D. degree in a related discipline (transportation engineering, computer engineering/science, or related disciplines) by December 2025. Expertise in AI, deep learning, and programming (e.g
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. Experience in, or willingness to learn data-driven approaches, including artificial intelligence (AI) and machine learning (ML) models, to solve problems. The Successful Candidate Will A curious scholar with a
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as an integrated analytical framework, we apply comparative risk assessment, disease modeling, machine learning, and survival extrapolation methods to systematically quantify the long-term impacts
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, economics, or a closely related field. Excellent knowledge of computational linguistic tools and machine learning techniques. Experience with data analysis with Python and STATA. Prior experience in
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opportunity to contribute to leading-edge research at the intersection of applied machine learning and clinical dental practice. As a member of our team, you will help translate contemporary data science
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hazards, enhancing asset protection, maritime security, emergency preparedness, and societal resilience. The project will leverage advanced AI and machine learning techniques to enable predictive risk
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About the Opportunity About the Institute Do you want to be part of an exciting new Institute focused on combining human and machine intelligence into working AI solutions? We are launching a
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | 19 days ago
analyses), patient monitoring algorithms (e.g., artificial intelligence/machine learning approaches) for disease detection and management, and physiologic closed-loop controlled devices. Research activities
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We are looking for a researcher, whose expertise lies in machine learning or uncertainty quantification, to work with Professor Richard Wilkinson on an EPSRC-funded project entitled “Scaling Cardiac