14 data-"https:"-"https:"-"https:"-"https:" PhD positions at University of Nottingham in United Kingdom
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The School of Computer Science at the University of Nottingham is pleased to invite applications for a fully funded PhD studentship in deployable, efficient, and trustworthy computer vision. This is
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support population-level prevention strategies. Further information: Applicants should have either a minimum 2.1 undergraduate degree in a relevant area (public health, epidemiology, economics, statistics
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to the interests of one of the School’s research groups: Cyber-physical Health and Assistive Robotics Technologies Computational Optimisation and Learning Lab Computer Vision Lab Cyber Security Functional
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mechanics. Skills you’ll gain Under the supervision of Charlie Heron and myself you will gain: Expert knowledge of centrifuge modelling challenges and solutions. Advanced programming/data analysis skills. How
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of experimental modelling challenges and solutions. Advanced programming/data analysis and simulation skills. How to effectively communicate complex and novel research. Opportunities to develop lab demonstration
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-edge adaptive mesh refinement techniques; a lightweight prediction tool developed upon the simulation data to predict key thermofluidic parameters for the design of high heat flux cooling components
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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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applicants who have a background or strong interest in Computer Science, interactive media, software engineering, 3D modelling/animation, VR/AR, human–computer interaction or related digital-tech fields
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global manufacturers. For details, visit the MTC website . For further information on this PhD position please contact Dr Sara Wang (Sara.Wang@nottingham.ac.uk ) Closing Date: 27th February 2026. Proposed
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We are seeking a research assistant with a background in computing to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device