27 algorithm-development-"University-of-Surrey" PhD positions at University of Cambridge in United Kingdom
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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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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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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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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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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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participants Ideally, practical skills in one of (a) programming, (b) machine learning, and/or (c) design Responsibilities Developing and conducting novel research projects individually and on teams Developing a
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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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, working closely with Professor Nora Pashayan. The successful candidate will focus on developing ethnicity-specific risk thresholds that more accurately reflect the variations in breast, ovarian, and