382 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" "St" PhD positions in United Kingdom
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advanced XCT workflows by developing Artificial Intelligence (AI) and Machine Learning (ML) tools to support imaging before the reconstruction phase. The research will focus on: Data Upscaling : Using ML U
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harvesting recent breakthroughs in Machine Learning (ML) and analytical modelling. Specifically, this project seeks to quantify key performance metrics and create powerful adaptive ML-driven management methods
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with NEOM, one of the world’s largest ecological restoration programmes, the project will develop machine-learning approaches to analyse satellite observations of vegetation change and evaluate large
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framework integrating physics-informed machine learning, scenario generation, and human-in-the-loop preference-based reinforcement learning to prioritise climate-robust and equity-aligned interventions
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programming (e.g., Python/C++), machine learning frameworks, or robotics software environments such as ROS. You are motivated to work in a multi-disciplinary research environment combining engineering, AI, and
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training programme at the start of the PhD to develop skills in areas such as programming, data analysis, machine learning and signal processing. This will provide the technical foundation required to work
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St George’s, please confirm on the form that your line manager is aware of your application. Selection process Short-listed candidates will be invited for interview. If you have not heard from us
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). Students will be actively encouraged to engage with and learn from the collaborating institutes in the programme, including Oxford, City St George’s, and London School of Hygiene and Tropical Medicine. Each
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across sociology, anthropology, migration studies and science and technology studies (STS). There will also be opportunities to engage with comparative insights from the Netherlands and Sweden. Funding and
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for operational decision‑making, interactive design, or control‑in‑the‑loop visualisation. Machine‑learning surrogates offer speed, yet purely data‑driven models often extrapolate poorly and may violate physical