333 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD scholarships in United Kingdom
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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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an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject Area Medical imaging, biomedical engineering, computer science & IT
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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
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they can reliably, affordably, and fairly support a net-zero energy system. The research will focus on how data-driven and machine-learning-based control can coordinate demand, storage, and local generation
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experts at University Hospitals Coventry & Warwickshire/NHS Trust. The research will involve emulating laparoscopic surgical tasks using a robotic platform under varying network conditions. Machine learning
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science and applications. This project aims to develop the required formalism using modern probabilistic and machine-learning approaches, reformulating the problem in terms of conditional probabilities
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machine learning techniques, you will identify patient subgroups, improve diagnostic accuracy, and develop a biomarker-based clinical decision support system to assist risk stratification and outcome