18 parallel-computing-numerical-methods-"Prof" PhD scholarships at University of Cambridge
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engineering, computational neuroscience, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from
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to develop novel, bio-inspired neural networks that flexibly and robustly control locomotion in multi-limbed robots. "Self-organised clocks for reliable spiking computation" (Supervisor: Prof Timothy O'Leary
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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
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will be tasked with the development of new models for the early detection of CIN cancers, applying bleeding edge computational methods and machine learning approaches to improve detection and
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please e-mail Prof Jeremy J Baumberg (jjb12@cam.ac.uk ). Please quote reference KA47350 on your application and in any correspondence about this vacancy. The University actively supports equality
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://www.postgraduate.study.cam.ac.uk/courses/directory/cvmdpdmed Quote the project title and supervisors (Dr Ana Vujic / Prof Antonio Vidal-Puig). Applications must include: CV Academic transcripts Statement of purpose (research
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letter (Max 3 sides, including proposed research project) to be submitted to Dimple Patel, dp692@medschl.cam.ac.uk by July 29th 2025. Informal enquiries should be directed to Prof Menna Clatworthy
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chromatin profiling methods along with CRISPR/Cas9-meduated cell line engineering and various animal models. You will study the effects of the activation or depletion of chromatin-modifying enzymes using
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the proteins associated with their binding sites with a view to understanding therapeutic mechanisms [e.g. see Nature Biotechnology 2023, 41 1265]. We are expanding this work to create methods to characterise
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. Current Mass Spectrometry approaches have been unable to assess most of the FOXA1 protein for PMTs, but new Mass Spectrometry methods such as 'top-down' approaches permit an unprecedented opportunity