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Application deadline: 15/08/2025 Research theme: Computer Science No. of positions: 1 Eligible for: UK This 4-year PhD project will be funded by DLA studentship and is open to UK students
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Grand Tours, one-day races (monuments and semi-classics), and select World Tour 1-week stage races. The data sources will include video and previous race commentary to ‘code’ key events in races that help
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of our key research directions relevant for this position. Candidates should have (or be near to completing) a PhD in Computer Science or a related subject, or relevant experience. A high degree of
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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker
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the PhD has been awarded. Fixed-term: The funds for this post are available for 18 months in the first instance. To apply online for this vacancy and to view further information about the role, please
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the PhD in Engineering programme. Make sure to reference the supervisor's name (Dr Farzad Hayati) in your application. Informal enquiries are welcome and should be directed to Dr Farzad Hayati at f.hayati
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ADTKD diagnostics and clinical trials. What you’ll do You will work at the interface of laboratory and data science, and clinical genetics. You will: Establish and characterise the UK ADTKD-MUC1 cohort
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and clinical phenotype information. Using these datasets, you will undertake comprehensive strategies aimed at the characterisation and therapeutic targeting of medulloblastoma biology, focussing
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abrasive slurries. The applicant will have obtained or be close to obtaining a PhD or have some years of relevant industrial experience in Chemical Engineering, Colloid Science, Mechanical Engineering
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will be helpful to have a working knowledge of multi-phase flows, and in particular of abrasive slurries. The applicant will have obtained or be close to obtaining a PhD in Chemical Engineering, Colloid