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- Tasks: Develop innovative statistical machine learning methods for predictive modelling in healthcare.
- Company: Join the Oxford–Novartis Collaboration for AI in Medicine, a leader in medical research.
- Benefits: Competitive salary, pension scheme, and opportunities for professional growth.
- Why this job: Make a real impact on patient outcomes using cutting-edge data science techniques.
- Qualifications: PhD/DPhil in Statistics or related field with expertise in statistical modelling.
- Other info: Collaborative environment with access to unique datasets and multidisciplinary teams.
The predicted salary is between 39424 - 47779 ÂŁ per year.
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.
Only applications received before 12 midday on 16 February 2026 will be considered. Please quote 184574 on all correspondence. ÂŁ39,424 to ÂŁ47,779 per annum. Research Grade 7. This is inclusive of a pensionable Oxford University Weighting of ÂŁ1,730 per year.
Postdoctoral Researcher in Biostatistics - Statistical Machine Learning in Oxford employer: Economicsnetwork
Contact Detail:
Economicsnetwork Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Postdoctoral Researcher in Biostatistics - Statistical Machine Learning in Oxford
✨Tip Number 1
Network like a pro! Reach out to your connections in the field of biostatistics and machine learning. Attend conferences or seminars where you can meet potential collaborators or employers. Remember, sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your work with statistical models and machine learning projects. This could be anything from GitHub repositories to presentations. It’s a great way to demonstrate your expertise and make a lasting impression.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss your experience with Bayesian frameworks and predictive modelling. Practise explaining complex concepts in simple terms – it shows you can communicate effectively with diverse teams.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Tailor your CV and supporting statement to highlight how your skills align with the role. Make it clear why you’re the perfect fit for the Oxford–Novartis Collaboration!
We think you need these skills to ace Postdoctoral Researcher in Biostatistics - Statistical Machine Learning in Oxford
Some tips for your application 🫡
Tailor Your Supporting Statement: Make sure to customise your supporting statement to highlight how your skills and experiences align with the specific selection criteria mentioned in the job description. Use clear examples to demonstrate your expertise in statistical machine learning and causal modelling.
Keep It Concise and Relevant: While it’s important to showcase your qualifications, keep your CV and supporting statement concise. Stick to relevant information that directly relates to the role, and avoid unnecessary details that might distract from your key achievements.
Showcase Collaboration Skills: Since this role involves working closely with a multidisciplinary team, emphasise your experience in collaborative projects. Highlight any instances where you’ve successfully worked with clinicians, statisticians, or other researchers to achieve common goals.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way to ensure your application is received and considered. Plus, it gives us a chance to see your enthusiasm for joining our team at StudySmarter.
How to prepare for a job interview at Economicsnetwork
✨Know Your Stuff
Make sure you brush up on your knowledge of statistical machine learning and causal inference. Be ready to discuss specific methodologies you've worked with, especially in Bayesian frameworks. This will show that you're not just familiar with the theory but have practical experience too.
✨Showcase Your Collaboration Skills
Since this role involves working closely with clinicians and other researchers, be prepared to share examples of how you've successfully collaborated in multidisciplinary teams. Highlight any projects where you contributed to study design or analysis plans, as this will demonstrate your ability to work effectively in a team environment.
✨Prepare Your Supporting Statement
Your supporting statement is crucial! Make sure it clearly addresses each selection criterion mentioned in the job description. Use specific examples from your past experiences to illustrate how you meet these criteria, particularly your expertise in handling large-scale datasets and implementing analytical methods.
✨Practice Explaining Complex Concepts
You’ll need to communicate complex ideas clearly, so practice explaining your research and methodologies in simple terms. Think about how you would describe your work to someone without a technical background. This will help you convey your ideas effectively during the interview.
