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FTE, 30 hours per week minimum). About you You’ll have a PhD (or equivalent experience) in a numerically focused field and a strong background in epidemiological and statistical methods. You’ll bring a
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an increasingly digitalized world: AI, data, and software are required to process complexity, present simplicity via user interfaces and experiences, whilst adhering to formal/ de-facto standards yielding unified
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) position on the project Aggregating Safety Preferences for AI Systems: A Social Choice Approach, funded by ARIA under the Safeguarded AI TA1.4 call. The project explores novel aggregation methods
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, develop and implement statistical analyses and prepare tables, figures and manuscripts for presentation and publication. To be considered for this role you will have a relevant PhD or a degree at Masters
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with tact and diplomacy, escalating matters appropriately; liaise with the Academic Administrator to undertake FHS end of year surveys and lecture surveys as appropriate; use various systems and software
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prioritise a varied workload, meet tight deadlines and maintain performance under pressure, without close supervision. Attention to detail and a methodical approach to work are crucial. You should have strong
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health data, development and testing of solutions using rigorous methods of evaluation recognised in Global Health, and a focus on highly scalable, cost-effective solutions that can be delivered at scale
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libraries (TensorFlow, Keras, and PyTorch), strong programming skills (Python and R), experience with statistical and mathematical methods for AI in high-dimensional spaces, and proficiency in developing
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correspondence. As part of your formal online application, you will be required to upload a CV and supporting statement. Only applications received before midday on 25 September 2025 can be considered.
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information and advice on best-practice methodologies in machine learning/deep learning. It is essential that you hold a PhD/DPhil (or close to completion) in a relevant quantitative field (e.g. biostatistics