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such as multiplex proteomics. The postholder will contribute to the planning and delivery of ATLAS studies, prepare and clean large-scale datasets, apply appropriate statistical methods, and assist in
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. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely related field) with significant post-qualification research experience. You will experience
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although not be limited to Target Trial Emulation and Mendelian randomisation. You will be part of a growing and dynamic team which is involved in many aspects of data science, epidemiology, statistics
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use statistical methods to identify similarities and discrepancies between experiments and simulations, as well as machine learning tools. The project itself falls into three separate “aims”, and the
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to exact solutions of the governing equations in smaller domains. This viewpoint is compelling: it connects dynamical events to statistical properties of the flow, it generates a set of rules by which
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quantitative proteomic data with a degree of statistical rigour using common freely available software packages; proficiency in handling large datasets including bioinformatics and biostatistics; and knowledge
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, viability and biological function. The work will require rigorous experimental design, appropriate controls and benchmarks, statistical analysis, traceable data and predefined acceptance criteria. The role
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internationally recognised research environment. It is essential that you hold, or be close to completing, a PhD/DPhil in computational biology, mathematics, statistics, bioinformatics, oncology or another related
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development About You You will hold a Ph.D/D.Phil in a quantitative or theoretical discipline (e.g. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely
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research questions and investigate trends in outcomes among people with diabetes. Advanced epidemiological and statistical methods will be applied, including causal inference approaches such as target trial