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About us Primary Supervisor: Dr Rebecca Baggaley Applications are invited for 1 PhD Studentship for students with a Home Fees status, based at the Institute of Health Informatics, UCL
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-based solutions (NbS) for water and wastewater treatment. The research will explore sustainable engineering strategies to boost their performance to deliver benefits for the environment and society. The
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for distribution electric propulsion. Who we are looking for We are looking for enthusiastic, self-motivated applicants with first-class degree in Electrical Engineering, Aerospace Engineering or Computer Science
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PhD Studentship: Sleep and Circadian Rhythms in Elite Sport (Co-funded by Brighton & Hove Albion FC)
at £1.2million project on Sleep and Mental Health. Dr Dominique Makowski with expertise in psychophysiology and computational neuropsychology. Dr Sean Figgins with a background and expertise in Sports Science, and
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propulsion. Who we are looking for We are looking for enthusiastic, self-motivated applicants with first-class degree in electrical engineering, Aerospace Engineering or Computer Science with good electrical
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networking with colleagues and students; planning and organising research resources and workshops. Successful applicants will have or be near to completing a PhD in computer science, information engineering
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networking with colleagues and students; planning and organising research resources and workshops. Successful applicants will have or be near to completing a PhD in computer science, information engineering
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the harmful effects of deepfakes on ordinary people. You will draw on methods from psychology, linguistics, data science, and computer science, such as behavioural analytics, neuroimaging, face and voice
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computer literacy, good inter-personal communications skills. Desirable skills: A Master in Health Economics with experience in cost effective analyses. Funding notes The three year studentship covers UK
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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging