Senior Postdoctoral Researcher in Biostatistics: Statistical Machine Learning

It is essential that you hold a PhD/DPhil in Statistics, Biostatistics, Statistical Machine Learning, or a closely related quantitative discipline, with substantial postdoctoral research experience and an established publication record in leading peer-reviewed journals. You must demonstrate advanced expertise in the development of statistical models and algorithms, particularly within Bayesian, generative, or probabilistic machine learning frameworks, together with deep knowledge of causal inference, prognostic modelling, and individualized treatment effect estimation. Extensive experience in implementing and validating complex models using statistical software such as R or MATLAB and programming languages including Python is required. You should also have a proven ability to provide scientific leadership, contribute to the development of competitive research funding applications, articulate complex methodological concepts to diverse scientific audiences, and work effectively across disciplinary boundaries.

This position is offered full time on a fixed term contract until 31 August 2027 and is funded by Novartis.

We are looking to appoint a Senior Postdoctoral Researcher to develop novel probabilistic statistical machine learning methods to build causal predictive models available in the one-of-a-kind Novartis-Oxford MS (NO.MS) dataset as part of Oxford-Novartis Collaboration for AI in Medicine. The NO.MS is the largest and the most comprehensive dataset on multiple sclerosis (MS), a collection of data on over 40,000 individuals measured longitudinally, some over a decade. Under the line management of Dr. Habib Ganjgahi and close collaboration with Professors Chris Holmes and Thomas Nichols, you will apply and develop state of the art causal scalable statistical machine learning prognostic models to identify factors and early change-parameters in clinical and MRI images that, on an individual patient level, contribute to a reliable prediction of time to long-term outcomes using clinical, laboratory and high-dimensional image data that can handle missing data and different data modalities and building individual treatment response models to predict which subjects will respond to treatment and heterogenous treatment effect. Whilst you will be predominantly based at the Big Data Institute, you will also be expected to spend time at the Department of Statistics and participate in the OxCSML research group in Statistics. You will be responsible for providing senior scientific leadership in the development, theoretical advancement, and application of state-of-the-art causal and probabilistic statistical machine learning methodologies for individual-level outcome prediction and treatment response modelling. You will lead methodological innovation using large-scale longitudinal clinical, laboratory, and high-dimensional neuroimaging data from the Oxford-Novartis Multiple Sclerosis (NO.MS) dataset, designing scalable predictive frameworks that explicitly address missingness, multimodal data integration, and heterogeneous treatment effects. You will play a central role in shaping statistical strategy within the Oxford-Novartis Collaboration for AI in Medicine, lead the formulation of statistical analysis plans, drive the production of high-impact peer-reviewed publications, and provide intellectual leadership in the supervision and mentoring of junior researchers and doctoral students.