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Field
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, Materials Science, or a related discipline. The successful applicant will demonstrate strong interest and self-motivation in the subject and the ability to think analytically and creatively. Good computer
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‑dependent data is often unavailable and many traditional monitoring methods are too resource‑intensive or unsuited to the complex, delicate habitats used by juvenile fishes. This project aims to offer both
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generate biologically interpretable models of zoonotic emergence and ultimately aid in developing new tools for human pathogen surveillance. This project offers training in bioinformatics, data science and
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reservoir heterogeneity. This project will integrate core observations, wireline logs and seismic data to develop geological and petrophysical models that constrain reactive reservoir simulations
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AI and data science, particularly in dynamic settings where observations are collected sequentially and decisions influence future outcomes. This project will develop novel machine learning and
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Announcing 10 PhD Studentships within the Royal Holloway Social Purpose Centre for Doctoral Training
interested in one of these, please get in touch with the project supervisor directly. To view potential projects, please visit the CDT’s webpage, here . Prepare application following the Applicant information
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Supervisor: Dr Giulia Biffi Course start date: 1st October 2027 Project details For further information about the research group, including their most recent publications, please visit their website
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predicts something important? This project will uncover the neural circuits that transform visual information into dopamine signals that teach the brain about motivation, action and decision-making
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and machine learning methodology to help deal with key challenges in developing such models in large-scale observational electronic healthcare record data. These models will be applied to important real
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across historical, social science, and data science methods. While this studentship is grounded in historical and archival practice, candidates who wish to engage with quantitative, digital, or comparative