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both sites. The project sits at the interface of cell line engineering, protein science and machine learning and you will receive advanced training in these areas while developing methods to accelerate
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Fixed-term: The funds for this post are available for 3 years. A doctoral studentship is available in the forthcoming Aspirational Computing Lab (February 2026) in the Department of Computer Science
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machines capable of exerting large forces. We have recently managed to construct these hybrids in proof-of-principle experiments. Our recent progress opens up many intriguing opportunities to make active
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for complex data accessible to the scientific community and to produce innovative methodology related to trial designs, longitudinal and event history data, precision medicine, causal inference, AI/machine
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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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combines hands-on training, cohort-based learning, and cutting-edge research, preparing graduates for careers in academia, industry, startups, and beyond. We welcome applicants from the Physical Sciences
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, Su J, Eldridge MD, Riedel A, Serrao E, Rodewald HR, Mack M, Shields J, Cohen ES, McKenzie ANJ, Goodwin RJA, Brindle K, Marioni JC, Halim TY. (2020) ILC2-driven innate immune checkpoint mechanism
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" (Supervisor: Prof Timothy O'Leary) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic
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positions may be filled once suitable candidates are identified. Fixed-term: The funds for this post are available for 4 years in the first instance, from 1 October 2026. Applicants are asked to upload
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for this post are available for 3 years in the first instance. To apply online for this vacancy and to view further information about the role, please click 'Apply' above. For any enquiries about the project