352 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" PhD positions in United Kingdom
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-engineering@kcl.ac.uk Department of Informatics : informatics-pgr@kcl.ac.uk Department of Mathematics : mathematics-pgr@kcl.ac.uk Department of Physics : physics-pgr@kcl.ac.uk Where to apply Website https
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emphasis on developing the payload for the ESTCube-LuNa mission. Building on previous demonstrations such as ESTCube-1, ESTCube-2, and Foresail-1p, the project aims to apply lessons learned to create a more
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funded. International applicants must demonstrate in their application how they will fund shortfall in fees by other means (personal funds,l or other grant); International applicants can visit https
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machine learning (ML) and artificial intelligence (AI) workflows, the project aims to create a comprehensive molecular atlas and identify novel, translational biomarkers and therapeutic targets. Project
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foundation in either machine learning or mathematical/computational neuroscience, demonstrable programming experience (Python/PyTorch), and the curiosity to work across disciplinary boundaries. A background in
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machine-learning-based surrogate models to accelerate design and control workflows. This PhD studentship would suit candidates with backgrounds or interests in engineering, physics, applied mathematics
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physics or computational data analysis (Python/R/MATLAB, machine learning, or bioinformatics) is highly desirable. Interested candidates should send a CV to michael.chappell@nottingham.ac.uk . Applications
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using multimodal approaches including advanced imaging, nano-mechanical characterisation and machine learning techniques Developing physics-informed reliability models using experimental datasets
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for translational biocatalysis, addressing critical needs in the development of sustainable biotechnologies. The programme will equip PhD students with advanced expertise in enzyme science, machine learning, enzyme
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on basic laparoscopic surgery tasks, using data collected under varying network conditions and applying machine learning and time-series modelling to predict delay. The models will be integrated into a real