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will study the development, adoption, and implications of digital technology and insurance—such as tools for capturing individualised data about behavioural risk factors and automating enforcement
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leaders, contribute to policy and process development, oversee data analysis and insights, and build strong partnerships across the University and with external providers to elevate the student experience
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. Required knowledge Python programming Machine learning background Image analysis Video analysis Audio analysis
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Identifying vulnerabilities in real-world applications is challenging. Currently, static analysis tools are concerned with false positives; runtime detection tools are free of false positives but
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related field. Demonstrated analytical skills, including proficiency with the R environment, spatial analysis and/or social research methods will be well regarded. This role offers a unique opportunity to
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weighted sum of the risks from tens to millions of independent disease-associated SNPs from across the genome. The conventional, or gold-standard, approach to analysis of GWAS data is to fit a regression
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-de Bruijn I., Kostine M., Kuijjer M., Bovee J., Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue
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Engineering The objective of this project is to design automated approach to detect bugs in various software, e.g., compilers, data libraries and so on. The project may involve LLMs. Required knowledge - self
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expertise in advanced econometric techniques, data modelling, and statistical analysis. The Master of Applied Econometrics program combines rigorous coursework with hands-on experience, equipping you with
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analysis skills - good communication skills (oral and writing) - good background in software testing and debugging - (can be obtained during project) a reasonable knowledge of AI systems Project funding