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Field
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) An interest in AI/ML and multi-omics integration. Solid background in statistical genetics and computational biology; experience with R, Python, or equivalent programming languages is essential. Passion
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model fitting, including Bayesian model fitting, is desirable but not essential. Familiarity or experience of management and analysis of large multidimensional real world data sets using Stata, R, Python
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research or industry experience will also be considered. Good statistical and analytical skills are necessary, ideally with the ability to work in R and experience with bioinformatic tools/software. Coding
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research or industry experience will also be considered. Good statistical and analytical skills are necessary, ideally with the ability to work in R and experience with bioinformatic tools/software. Coding
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cancer genomics and functional interpretation of genetic variants Proficiency in Python, R, or other bioinformatics languages Knowledge of cloud computing, and high-performance computing (HPC) environments
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sufficient specialist knowledge in transcriptomics and NGS sequencing as well as the essential computational skills (R, Unix or Python) to develop research programmes in this area, and familiarity with
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both potential and performance relative to opportunities when assessing suitability for the role. Applications close Wednesday, 27 August 2025 at 11.00pm AEST (R-54282). Please note that interviews have
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package such as Stata/R. A commitment to multi-disciplinary and collaborative working is also essential. The ideal candidate would have experience using electronic health records for pharmacoepidemiologic
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package such as Stata/R. A commitment to multi-disciplinary and collaborative working is also essential. The ideal candidate would have experience using electronic health records for pharmacoepidemiologic
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skills and experience: Essential criteria PhD qualified in relevant subject area Experience working with large datasets e.g. CPRD or similar Experience with relevant statistical software (STATA or R