Senior Postdoctoral Researcher in Statistical Machine Learning and Deep Generative Modelling


Company:	University of Oxford
Salary:	£49,119 - £50,552
Hours:	Full-time
Location:	Oxford, OX1 3LB
Working pattern:	Hybrid - 2 days remote
Job type:	Contract
Posting date:	30 Jul 2026
Closing date:	17 Aug 2026

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Summary

We are seeking to appoint a Senior Postdoctoral Researcher in Statistical Machine Learning and Deep Generative Modelling to apply and develop cutting- edge deep generative probabilistic models including conditional diffusion and flow matching models for synthesising Magnetic Resonance Imaging (MRI) and predictive analysis. This work will focus on the unique Novartis Oxford MS (NO.MS) dataset, the largest and most comprehensive dataset on multiple sclerosis (MS), encompassing longitudinal data from over 40,000 individuals, some tracked for more than a decade.

You will be responsible for advancing and applying state-of-the-art probabilistic deep generative models, including conditional diffusion and flow matching approaches, to generate advanced MRI modalities from conventional clinical scans. You will also develop predictive models capable of characterising disease progression and forecasting individual outcomes in multiple sclerosis under different treatment exposures. Beyond your own research, you will contribute to the design and delivery of collaborative projects, working closely with clinicians, imaging experts, and computational scientists across the Oxford–Novartis Collaboration for AI in Medicine. This will involve planning and managing your own programme of work, advising on model development strategies, finalising statistical analysis plans, and ensuring the timely completion of high-quality data analyses. You will also be expected to publish your findings in leading peer-reviewed journals, present your work at national and international conferences, and engage actively in seminars and collaborative discussions.

You must hold a PhD/DPhil in Statistics, Statistical Machine Learning, Deep Generative Modelling, or a closely related field, together with relevant postdoctoral research experience. You will bring extensive expertise in the development and application of conditional diffusion models, flow matching techniques, or related generative approaches, as well as experience working with high-throughput data in the context of human disease. Strong programming skills are essential, particularly in Python, R, or MATLAB, alongside familiarity with Bayesian methods and statistical modelling. A proven record of publishing in high-quality journals and presenting at scientific meetings will be required, demonstrating your ability to lead and deliver impactful research.

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