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and Technology (CST) at the University of Cambridge. The goal of this PhD programme is to launch one "deceptive by design" project that combines the perspectives of human-computer interaction (HCI) and
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non-clinical PhD studentship in cardiometabolic research, commencing October 2026 in the Department of Medicine (VPD Heart & Lung Research Institute), University of Cambridge. The project will be based
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metabolomics, as well as imaging mass cytometry, and test the effect of AZ clinical compounds on the immune-metabolic landscape of our PanIN-to-PDAC models. Dr Koulman will provide training and support with
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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student
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transcriptomics and histone mark profiling as well as by live imaging approaches. As part of this project, you will have the opportunity to gain computational data analysis skills. This studentship comes with
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Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country United Kingdom Application Deadline 15 Sep 2025 - 23:59 (Europe/London) Type of Contract Temporary Job Status Full-time Offer
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@ch.cam.ac.uk , including 'PhD CI ¿ October 2026' in the subject. How to apply Please apply via the University Applicant Portal. For further information about the course and to access the Applicant Portal, click
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laboratory mice throughout the PhD is an essential requirement. Funding This four-year studentship includes full funding for University fees and an index-linked stipend starting at £22,500 for four years
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Two fully-funded 3-year PhD studentships are available in Neuromorphic and Bio-inspired computing at the interface between control engineering, electrical engineering, computational neuroscience
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Supervisors: Professor Sir Steve Jackson and Dr Mark O'Connor (AZ Partner) Course start date: 1st October 2026 Project details Targeted Alpha Therapy (TAT) selectively delivers high Linear Energy Transfer (LET) alpha-particles to cancer cells, maximising efficacy while minimising toxicity. ...