386 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" "St" PhD positions in United Kingdom
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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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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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considered for Research Assistant. Strong programming expertise in Python and C++, with experience developing real-time robotic and AI systems. Experience in deep learning and computer vision, including
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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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integrating machine learning, computational modelling, and experimental validation. The successful candidate will receive training in both computational and experimental biology within a highly collaborative
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using R scripting, Python and machine learning. The successful candidate will be expected to participate in both laboratory and informatic research but the emphasis will depend on the student’s prior
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The Centre for National Training and Research Excellence in Understanding Behaviour (Centre-UB) in partnership with the King’s Centre for Military Health Research (KCMHR), King’s College London (KCL
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feedback using machine learning and control engineering methods. The project will be hosted at the Bristol Robotics Laboratory (BRL), the UK’s largest academic centre for robotics research, with access
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into the sample of interest. Recently we have been using AI and machine learning to predict the distortion present and significantly speed up this correction process. This PhD project will take the latest in AI
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. Strategies will centre on improved formulations of the mixed-integer constraints, as well as the use of machine learning to accelerate conventional solution algorithms (e.g. branch and bound). The second goal