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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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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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. 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
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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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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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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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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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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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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