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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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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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situ, with direct structure determination, and (ii) investigating and optimizing methods for chirality determination using electron crystallography. Candidate We are looking for a highly motivated and
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technologies, bulk and single-cell RNA-sequencing, flow cytometry, multiplex immunofluorescence, and standard molecular biology and biochemistry techniques. A computational component may also be available
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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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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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the context of computing Familiarity with research tools and methods, including statistics platforms like R and/or thematic analysis Knowledge of user-centred design and research methods involving human
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on the research topic and relevant methods. By the second half, the candidate will take on a leading role and begin carrying out the research comprising their doctoral dissertation. The candidate is expected
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comprehensive model of what tranquillity is, the factors that influence it and how to design for it. Attention to design contexts and design processes will be key to ensuring that useful measurements, methods and
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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