980 phd-studenship-in-computer-vision-and-machine-learning PhD positions in United Kingdom
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Unit at the University of Cambridge provides an ideal place to commence a career in Biostatistics. Our thriving PhD programme is hugely successful and competition for places is high. The BSU's relaxed
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crucial technique used in industries to assess materials, components, and structures without causing damage. This PhD project focuses on advancing Ultrasound Testing and X-ray Computed Tomography (CT) – two
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The University of Exeter has a number of fully funded EPSRC (Engineering and Physical Sciences Research Council ) Doctoral Landscape Award (EPSRC DLA) studentships for 2026/27 entry. Students will be given sector-leading training and development with outstanding facilities and resources. The...
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This PhD project aims to address one of the key challenges in the manufacturing industry, the increase in productivity by utilizing the equipment with its optimum performance and without any
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both sites. The project sits at the interface of cell line engineering, protein science and machine learning and you will receive advanced training in these areas while developing methods to accelerate
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Energy Storage! Fully funded 4-year PhD Studentship in Chemistry - Machine Learning-Accelerated Quantum Chemical Modelling of Molecular Junctions and Surface Catalytic Reactions PhD Studentship: Taking
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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3.5-year D.Phil. studentship Project: Computational Fracture Mechanics Supervisors: Prof Emilio Martínez-Pañeda Oxford’s Mechanics of Materials Lab is seeking a PhD (DPhil) student to support the
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Applications are invited for a University of Warwick PhD Studentship in The Department of Computer Science in collaboration with the Department of Psychology. The PhD will start October 2026
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Productivity Index (RPI) using observed versus potential productivity modelled with machine learning (https://doi.org/10.1016/j.ecolind.2025.113208 ), this applied geospatial ecology project will study how