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publications (not necessarily expected at this level) • Grades at relevant university-level courses (population genetics, evolutionary biology, bioinformatics, statistics, programming, wet-lab molecular
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backgrounds and levels of seniority. Experience with research data management and statistical analysis using statistical software (SPSS). Demonstrated and applied knowledge/understanding of HREC and regulatory
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of multi-modal datasets, including MRI/fMRI, behavioural and audio data Develop reproducible statistical and machine learning analysis pipelines Support grant writing, ethics submissions and project
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Data Analytics, including but not limited to: “Data Analytics for Social Research”, “Applied Analytical Statistics for Social Scientists”, “Machine Learning with Social Data” and “Social Networks
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• PhD in Mathematics/Statistics/CS. Skills: • Proficiency in reinforcement learning, quantitative finance, risk management, and machine learning. • Strong programming skill in Python
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opportunities for people traditionally under-represented in mathematical education research and to enabling everyone to flourish. You must have expert knowledge of quantitative research methods and statistical
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statistical methods, computational models and/ or machine learning algorithms to analyse environmental and infectious disease data; - Collaborate with multidisciplinary and international teams of researchers
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lead the design, statistical optimisation and validation of assays for clinically relevant bladder cancer targets. Their central objective will be to develop an algorithmic workflow to detect new target
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. Collaborating with scholars from computer science and statistics to support the multidisciplinary research of economic world models. Promoting the research on economic world models in the world-leading finance
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and apply statistical models for ageing outcomes including biological age clocks, frailty indices, and multisystem ageing trajectories Contribute to the translation of epidemiological findings