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Project Link: PhD Studentship in Artificial Intelligence in Medical Imaging and Diagnostics | Project Opportunities | PhD | University of Leeds Eligibility: UK/International Funding: School of
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£19,569 (tax free) + £7,500 industrial top-up. Combinatory Artificial Intelligence (also known as Third Wave AI as initially described by DARPA) is the term that references the next foreseen advances within
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solely on the safety of artificial intelligence (AI). Are you interested in how law should adapt to AI, and its potential role in regulating the AI revolution? Previous industrial revolutions and the
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of artificial intelligence (AI) nowadays, it has become possible to develop a fast-response AI-based condition monitoring system for gas turbine engines. The objective of the project is to develop novel AI-based
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Discover your career The world of the University of Nottingham is defined by our people and the values we share. Our environment is an ambitious vision brought to life across vibrant and forward-thinking global campuses. An ever changing world where open minds and diverse cultures are able to...
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languages such as Python, and experience of Artificial Intelligence and machine learning. They will also be familiar with working with real world data and analysing data based on large and diverse datasets
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field of Artificial Intelligence, commencing 1st October 2025. The studentships will have a duration of 42 months (3.5 years), with an annual stipend at the UKRI rate (currently £20,780) and £3000 p.a
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PhD Studentship in Artificial Intelligence in Rheumatology Project title: Harnessing Artificial Intelligence to Transform the Treatment of Inflammatory Arthritis Supervisors: Professor Weizi Li
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Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering Research Group) Aim: Develop a mathematical model for analysing reliability and efficiency of medical processes Background Adverse events and preventable failures in healthcare services represent a key area of...
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PhD studentship: Improving reliability of medical processes using system modelling and Artificial Intelligence techniques Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience