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to cancer biology, as well as a strong commitment of developing and using new tools to address cutting-edge questions in these fields. This studentship is embedded within the piRNA team, consisting of both
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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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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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will be defined, decomposed and assessed through attention to artefacts and practices across a range of sectors and disciplines. Focusing on the automotive context, the project will develop a
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nonlinear control and optimisation to develop novel, bio-inspired neural networks that flexibly and robustly control locomotion in multi-limbed robots. "Self-organised clocks for reliable spiking computation
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used. AI methods for generating regulatory hypotheses between genes, hormones and physical properties will also be developed. Applicants must have/be close to obtaining a PhD or MPhil in Computational
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of our approach is the innovation of novel methods to investigate genome function. For example, we have recently developed ways to map the binding of nucleic acid-interacting drugs and small molecules
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therapy (Simpson et al. in preparation*). When these local metabolic / immunologic changes happen during pancreatic cancer evolution remains unknown. More importantly, whether these spatial changes can be
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to better understand community perspectives and identify culturally appropriate engagement approaches. Prepare the ethics application and develop participant-facing materials. Contribute to the public
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target, since all known treatment resistance mechanisms are downstream of, and dependent on FOXA1. However, FOXA1 has been a difficult protein to study for technical reasons. We have developed a novel tool