University of Oxford  Jobs

University of Oxford

Applications Close:

Big Data Institute, Li Ka Shing Centre for Health and Information Discovery, Old Road Campus, Headington, Oxford, OX3 7LF

5 Star Employer Ranking

"Postdoctoral Researcher in Biostatistics - Statistical Machine Learning"

Academic Connect
Applications Close
Is this job right for you? View Vital Job Information and Save Time

Postdoctoral Researcher in Biostatistics - Statistical Machine Learning

Research Grade 7

16-Feb-2026 12:00

Location

Oxford, OX3 7LF / OX1 3LB, UK

University of Oxford

Type

Full-time fixed-term until 31-Aug-2027

Salary

£39,424 - £47,779 per annum

Required Qualifications

PhD/DPhil in Statistics, Biostatistics, Statistical Machine Learning
Bayesian/probabilistic frameworks
Causal inference & predictive modelling
Python/R/MATLAB experience
Multidisciplinary collaboration

Research Areas

Statistical Machine Learning
Causal Predictive Models
Multiple Sclerosis (NO.MS Dataset)
Neuroimaging Analysis
Treatment Response Prediction
79% Job Post Completeness

Our Job Post Completeness indicates how much vital information has been provided for this job listing. Academic Jobs has done the heavy lifting for you and summarized all the important aspects of this job to save you time.

Postdoctoral Researcher in Biostatistics - Statistical Machine Learning

Postdoctoral Researcher in Biostatistics - Statistical Machine Learning

Location: Big Data Institute, Li Ka Shing Centre for Health and Information Discovery, Old Road Campus, Headington, Oxford, OX3 7LF. Additional location: Department of Statistics, 24-29 St Giles’, Oxford, OX1 3LB.

We are seeking to appoint a 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 provide probabilistic machine learning expertise to the Oxford–Novartis Collaboration for AI in Medicine, contributing to the study design and analysis of data alongside the development and application of new analytical methods independently or in collaboration with others. This post will be a key part of the core Oxford analysis team working in collaboration with imaging specialists and other biostatistics and machine learning researchers to deliver optimal research for the collaboration.

You will be responsible for the development, implementation, and evaluation of advanced causal and probabilistic statistical machine learning methodologies for individual-level outcome prediction and treatment response modelling. You will work with large-scale longitudinal clinical, laboratory, and high-dimensional neuroimaging data from the Oxford–Novartis Multiple Sclerosis (NO.MS) dataset to construct scalable prognostic and predictive models capable of handling missing data and heterogeneous data modalities. The role will involve close collaboration with clinicians, statisticians, and machine learning researchers, contributing to study design, statistical analysis plans, and the dissemination of findings through peer-reviewed publications, conference presentations, and internal scientific reports within the Oxford–Novartis Collaboration for AI in Medicine.

It is essential that you hold a PhD/DPhil (or are close to completion) in Statistics, Biostatistics, Statistical Machine Learning, or a closely related quantitative discipline, with demonstrated expertise in statistical model development and algorithmic methodology, particularly within Bayesian or probabilistic frameworks. You must have strong knowledge of modern computational statistics, generative models, causal inference, and predictive modelling, alongside experience in implementing analytical methods using statistical software such as R or MATLAB and scripting languages including Python. The ability to communicate complex methodological concepts effectively and to work collaboratively within a multidisciplinary research environment is essential.

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 184574 on all correspondence.

Contact Person: Bogdan Tiritelnicu
Vacancy ID: 184574
Closing Date & Time: 16-Feb-2026 12:00
Pay Scale: RESEARCH GRADE 7
Contact Email: recruitment@ndm.ox.ac.uk
Salary: Research Grade 7: Salary in range £39,424 - £47,779 per annum. This is inclusive of a pensionable Oxford University Weighting of £1,730 per year.

Tell them AcademicJobs.com sent you!

Apply Now

Frequently Asked Questions

🎓What qualifications are required for this Postdoctoral Researcher in Biostatistics role?

You must hold a PhD/DPhil (or be close to completion) in Statistics, Biostatistics, Statistical Machine Learning, or a related quantitative field. Essential skills include expertise in Bayesian/probabilistic frameworks, causal inference, predictive modelling, and proficiency with R, MATLAB, or Python. Strong communication in multidisciplinary teams is key. For tips on thriving in postdoc roles, see postdoctoral success guide.

🔬What are the main responsibilities in this Statistical Machine Learning postdoc?

Develop causal scalable statistical machine learning models for prognostic predictions and treatment response using the NO.MS dataset (40,000+ MS patients). Handle longitudinal clinical, laboratory, and high-dimensional MRI data with missing values. Collaborate with Dr. Habib Ganjgahi, Profs. Chris Holmes & Thomas Nichols on study design, analysis, and publications. Participate in OxCSML research group. Explore research jobs for similar opportunities.

📝How do I apply for this Oxford Biostatistics postdoc position?

Apply online via University of Oxford portal, uploading a CV and supporting statement addressing selection criteria with examples. Quote Vacancy ID: 184574. Deadline: 12 midday, 16 February 2026. No other documents initially. Use our free CV template and academic CV guide to strengthen your application.

💰What is the salary and contract details for this role?

Salary: Research Grade 7: £39,424 - £47,779 per annum (includes £1,730 Oxford University Weighting, pensionable). Full-time fixed-term contract until 31 August 2027, funded by Novartis. Check university salaries for comparisons.

📍Where is this position located and what are the work expectations?

Primarily at Big Data Institute, Li Ka Shing Centre, Old Road Campus, Headington, Oxford OX3 7LF, with time at Department of Statistics, 24-29 St Giles’, OX1 3LB. Expect multidisciplinary collaboration in Oxford–Novartis AI in Medicine. Review postdoc jobs in Oxford for relocation insights.

🧠What is the NO.MS dataset and project focus?

The NO.MS dataset is the world's largest MS collection (>40,000 individuals, longitudinal over a decade). Project builds individual-level causal models for long-term outcomes and heterogeneous treatment effects using clinical, lab, and MRI data. Ideal for probabilistic machine learning experts.
402 Jobs Found

Odessa College

201 W University Blvd, Odessa, TX 79764, USA
Academic / Faculty
Add this Job Post to Favorites
Closes: Apr 5, 2026
View More