45 phd-studenship-in-computer-vision-and-machine-learning PhD positions at University of Nottingham
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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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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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(worth approx. £15-17k across full PhD programme). Monthly stipend based on £20,780 per annum, pro rata, tax free. Application deadline: 4 January Person Specification (What we are looking for) We welcome
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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University of Nottingham – School of Chemistry PhD roles 2026: Multiple projects The School of Chemistry at the University of Nottingham is recruiting ambitious, curious and highly motivated
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understanding of the interactions between sweetness and flavour intensity in frozen desserts. The successful candidate will gain a multidisciplinary experience, learning aspects of sensory evaluation
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Subject area: Drug Discovery, Sustainability, Laboratory Automation, Microfluidics, Machine Learning Overview: This highly interdisciplinary 4-year funded PhD studentship will contribute to cutting
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approx. £15-17k across full PhD programme). Monthly stipend based on £20,780 per annum, pro rata, tax free. Working hours: Full-time (minimum 37.5 hrs per week). Working style: Primarily in-person at host
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(CHF), tailored to complex geometries typical of fusion reactor cooling systems. Compile a comprehensive dataset of boiling parameters to support machine learning-based analysis of two-phase flow
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This PhD is part of the The Fusion Engineering Centre for Doctoral Training (CDT), at the University of Nottingham and has industrial collaboration, being co-supervised by a member of staff at