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highly motivated and independent candidate with strong analytical skills. The candidate should have a strong desire to develop novel computational methods and ML/AI tools to address challenging problems
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, at the University of Cambridge, UK. The Postdoc will work together with a team of students and research collaborators on the development of learning-based discovery of robot task/environment designs
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. Insights gained will inform the development of novel therapeutic strategies for HFpEF and broader cardiometabolic disease. Training & Development The successful candidate will receive broad training in
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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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dynamics and tissue morphogenesis during embryo development using cellular, molecular and mechanical approaches. Cell movements underlie tissue patterns and shapes. Using chick embryos as the model system
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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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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
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Ascl1 are important. We have undertaken a comprehensive discovery experiment to identify all the proteins that can physically interact with Ascl1, using a method we developed called RIME (Rapid
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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