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Medical School. In August 2024, we welcomed our first intake of the NTU MBBS programme, that has been recently enhanced to include themes like precision medicine and Artificial Intelligence (AI) in
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long-term growth and external funding strategy. The JSB AI Center brings together faculty, students, and collaborators from business, information systems, engineering, computer science, communication
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | 1 day ago
: The Artificial Intelligence Regulatory Science Program conducts regulatory science research ensuring safe and effective AI/ML-enabled medical devices across rapidly expanding healthcare applications including
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-intensive Universities? As a Research Fellow in Computational Climate Politics you will work on the UKRI FLF project “Understanding normative change to address the climate emergency ”. This is a 4+3 grant
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biggest challenges. We are seeking a Research Fellow in Liquid Metal Artificial Enzymes to join the School of Electronics and Computer Science (ECS) at the University of Southampton. The successful
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available in the further tabs (e.g. “Application requirements”). Programme Description Annually, the Canon Foundation in Europe grants up to 15 fellowships to European researchers to enable them to continue
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for Aumovio through close collaboration with its business units. This research position is related to Project C3 “Communication over In-Vehicle Powerline” of the Aumovio-NTU corporate lab Phase 2 program. We
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(D^3) Institute and the LISH/Data Science & AI Operations Lab seek enthusiastic Postdoctoral Fellows skilled in computer science, statistics, operations research, or related computational fields. As
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will contribute to technical deliverables and help to plan for technology translation. Basic Qualifications PhD in engineering, computer science, biomechanics, or a related field. Additional
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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during