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submitting a cover letter explaining how your experience aligns with the role, your CV, and contact information for two referees. Only applications received before midday on the closing date will be considered
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blood samples for ex vivo and in vitro assays, with relevant specialist knowledge and experience designing, executing, and analysing data from single-cell & spatial RNA/TCR-seq experiments. Applications
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imaging and spectroscopy. Experience with data analysis or the ability to perform basic biochemical work with proteins and DNA, e.g., fluorescence labelling, enzymatic reactions will be highly rated in
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techniques, and biological data analysis. You will have highly effective verbal and written communication skills with all level of staff staff and an ability to operate effectively in a demanding and busy
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work on the PANGEA-HIV project, analysing viral sequence data to assess how effective broadly neutralising antibodies may be against current HIV strains in Southern Africa. Second, you'll support early
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longitudinally collected sequencing datasets (both Illumina and Oxford Nanopore) of clinical isolates that can be linked to electronic healthcare record data and/or metagenomic data. These unique datasets provide
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socioeconomic inequality. DIVREP involves three broad objectives: (1) assembling data on childbearing age and fertility levels, and estimating novel indicators of variation in reproduction for over 120 countries
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Natalia Ares (email: natalia.ares@eng.ox.ac.uk) For more information about working at the Department, see www.eng.ox.ac.uk/about/work-with-us/ Only applications received before midday on the 14th October
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statistical and computational methods designed to use “big data” and to address questions of direct or indirect relevance to common complex diseases and disorders. The appointee will join the group of Professor
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model and data biases; (2) build and evaluate XAI tools for external auditing and red-teaming; (3) generate predictive explanations without accessing model internal; (4) providing insight into model