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, UTRs, and transcription factor binding sites to breast cancer susceptibility. Using whole‑genome sequencing data from the UK Biobank, the 100,000 Genomes Project and other datasets, the student will
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of rare non coding regulatory variants, including those in enhancers, promoters, UTRs, and transcription factor binding sites to breast cancer susceptibility. Using whole genome sequencing data from the UK
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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experimental fluid dynamics. Experience with laboratory instrumentation, data acquisition, and coding for experiment control or data analysis (e.g. LabVIEW, MATLAB, or Python) would be beneficial. Applicants
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Master's thesis -An interest in turbine aerodynamics, secondary flows, and real-engine flow physics -Programming and data-analysis skills (Python preferred) -Curiosity and motivation to work on complex
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production processes and are actively exploring the incorporation of new materials, technologies and designs in their operations to achieve zero-carbon construction elements. The construction industry is under
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Distinction at postgraduate level) and experience conducting research with human volunteers. Experience with quantitative data analysis or experience with coding/programming is desirable, as is knowledge