Senior Postdoctoral Researcher in Biostatistics: Statistical Machine Learning

University of Oxford; Peacehaven; On-site; GBP 40,000 - 55,000; Full time

Job summary

A prestigious research institution in the UK is seeking a Senior Postdoctoral Researcher to develop probabilistic statistical machine learning methods within the unique Novartis-Oxford MS dataset. This role requires advanced expertise in statistical models, causal inference, and the ability to lead innovative research. Applicants should have a PhD/DPhil in a relevant field and substantial postdoctoral experience, with a focus on developing predictive frameworks for clinical outcomes. This position offers a full-time, fixed-term contract until August 2027, funded by Novartis.

Qualifications

• PhD/DPhil in Statistics, Biostatistics, or related quantitative discipline.
• Substantial postdoctoral research experience.
• Established publication record in peer-reviewed journals.

Responsibilities

• Develop novel probabilistic machine learning methods.
• Provide senior scientific leadership in statistical methodologies.
• Lead formulation of statistical analysis plans and publications.

Skills

Statistical model development

Bayesian machine learning

Causal inference

Prognostic modelling

Python programming

Statistical software (R, MATLAB)

Education

PhD/DPhil in Statistics or related field

Tools

R

MATLAB

Python

Job description

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 the Oxford–Novartis Collaboration for AI in Medicine. The NO.MS is the largest and 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 build 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.

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.

Applications for this vacancy should be made online and you will need to upload a supporting statement and CV. Your supporting statement must explain how you meet each of the selection criteria for the post using examples of your skills and experience. Please restrict your documentation to your CV and supporting statement only. Any other documents will be requested at a later date.

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

Only applications received before 12 midday on 16 February 2026 will be considered. Please quote 184573 on all correspondence.