Machine Learning Scientist

Nuffield Department of Primary Care Health Sciences, Radcliffe Observatory Quarter, Woodstock Road, Oxford Applications are invited for a Machine Learning (ML) Scientist, to work with a team working with Professor Sir Aziz Sheikh on a research programme on the HDR UK Inflammation and Immunity Driver (DP2), which seeks to transform the UK's capabilities to improve understanding of health mechanisms and outcomes, using respiratory and allergic disorders as exemplar domains. The successful applicant will develop and carry out their own aspect of work within the driver program, to ultimately plan and lead a high-quality programme of research using large cohort electronic health data with a focus on leveraging advanced AI/ML computational techniques to analyse complex healthcare data, develop predictive models, and build intelligent systems that can support automated data curation and linkage across multiple data sets and across four nations that will ultimately inform respiratory relevant policy. Responsibilities will include adapting existing and developing new research methodologies in AI/ML models for data curation across multiple datasets, patient stratification, and trial optimization such as developing risk-based stratification, monitoring and safety prediction systems.

You will have a PhD (or near completion) in computer science, data science, AI/ML, digital health, medical statistics, or a closely related field, with substantial practical experience in health data, a strong understanding of deep learning, neural networks, and traditional machine learning models and experience in software development (e.g. GitHub workflow, code review, and software testing). You will be based in the Radcliffe Primary Care Building, Nuffield Department of Primary Care Health Sciences, Radcliffe Primary Care Building, Woodstock Road, Oxford, OX2 6GG as your normal place of work but there may be the opportunity for occasional remote working. The position is funded by HDRUK, for two years in the first instance, and is offered full-time, although part-time working, to a minimum of 0.8FTE, will be considered.