589 assistant-professor-computer-science-data-"https:"-"https:"-"https:" PhD positions in United Kingdom
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, or even computational architectures. What we are looking for: Research Associate: hold a PhD (or equivalent) in Aerospace Engineering or a closely related discipline. Research Assistant: A first/masters
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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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subjects of this PhD programme including process intensification and green chemistry. Number of awards: 1 Start date: 1st October 2026 Award duration: 4 years Sponsor: EPSRC Supervisors Professor Kamelia
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computer scientists to design paradigms that compare "active" learning (standard VR) against "proprioceptive" learning (haptically guided movement), measuring outcomes such as path efficiency, force
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
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at Research Assistant) in Computational Biology, Microbiology, Bioinformatics, Computer Science, Data Science, Biological Sciences or related subjects for appointment at Research Associate Newcastle University
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. Reporting to Peter Visscher. To be considered, you must hold a PhD/DPhil in statistical genetics, quantitative genetics, bioinformatics, computer science, statistics, econometrics or another relevant field
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the deadline. This PhD project addresses a fundamental challenge at the interface of biomaterials science, sustainability, and biofabrication: transforming underutilised animal-derived waste into high-value
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aid referee decision-making there are many other potential applications for this type of technology and data. The ability to capture the detailed movement of 22 players and officials around the entire
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CD3 is a new, multidisciplinary and multi-institutional strategic national research programme dedicated to using data to transform our understanding of cancer risk and enable early interception