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A PhD studentship awarded by the Freeman Air and Space Institute (FASI) to support the Institute’s research and capacity building work in the field of air power. Award details In an increasingly
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for cardiovascular applications. You will build on our group’s expertise on Physics-Informed Machine Learning (PIML), a powerful approach that combines data-driven AI with the rigour of physical and physiological
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Applications are invited for a PhD studentship in the Department of Computer Science at City, University of London. The successful candidate will work on developing a novel AI-powered conversational
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for cardiovascular applications. You will build on our group’s expertise on Physics-Informed Machine Learning (PIML), a powerful approach that combines data-driven AI with the rigour of physical and physiological
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A PhD studentship awarded by the Freeman Air and Space Institute (FASI) to support the Institute’s research and capacity building work in the field of air power. Award details In an increasingly
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, including remote sensing Expertise in cloud physics, preferably cirrus clouds Aircraft performance/emissions modelling preferred but not required Creative problem-solving skills and ability to work
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academic performance in undergraduate and, where applicable, Master’s coursework Advanced programming skills in R and Python Familiarity with econometric techniques, especially panel data methods Ability
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/emissions modelling preferred but not required Creative problem-solving skills and ability to work independently *Candidates with a PhD in other disciplines may be eligible if they can demonstrate exceptional
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/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition, applicants should be highly motivated, able to work independently, as
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should have: Undergraduate or Master’s degree in Economics or another quantitative discipline (completed by start date). Advanced programming skills in Python and either R or STATA. Willingness and ability