42 assistant-professor-computer-science-data PhD positions at University of Nottingham
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, enabling a more stable and efficacious drug delivery over conventionally dosed medicine. This work integrates high data-density reaction/bioanalysis techniques, laboratory automation & robotics and machine
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computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and ML. The University
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computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and ML. The University
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3-year PhD studentship: Scaling-Up Functional 3D Printing of Devices and Structures Supervisors: Professor Richard Hague1 , Professor Chris Tuck1 , Dr Geoffrey Rivers1 (1 Faculty of Engineering) PhD
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Deadline: 11:59pm, 30th November 2025 Host University: University of Nottingham School/department: Engineering Start date: Wednesday 1st April 2026 Funding offer: Tuition fees covered in full (worth
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record, in many areas of breast cancer including pathology; cell, molecular and radiation biology/radiotherapy; medical oncology/chemotherapy; translational oncology; clinical trials; pre-clinical models
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-developed research support base including NET2Zero CDT, and EPSRC Centre for Doctoral Training in Resilient Chemistry: Feedstock to Function. The research programme will use a mixture of computational
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physiologically relevant models e.g. human airway epithelial air liquid interface using cells isolated from different patient groups combined with molecular biology approaches to mechanistically determine the key
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Location: Mechanical and Aerospace Systems Research Group, Faculty of Engineering, University of Nottingham Funding: UK Home fees + tax-free stipend of £24,000 p.a. for 4 years Applications
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in a more accurate analysis of optimizing the service performance. Computer vision approaches such as ones for object identification and action recognition can help to automatically identify deviations