At a Glance
- Tasks: Develop cutting-edge statistical methodologies and collaborate on exciting research projects.
- Company: Join the School of Mathematical Sciences at Lancaster University.
- Benefits: Competitive salary, supportive environment, and opportunities for professional growth.
- Other info: Dynamic team with a focus on diversity and inclusion.
- Why this job: Make a real impact in multivariate extreme value theory and publish your findings.
- Qualifications: PhD in statistics or related field, strong programming skills, and research experience.
The predicted salary is between 39906 - 46049 £ per year.
This grant focuses on developing understanding of geometric representations of extremes, and how these can be used to provide state-of-the-art statistical methodology for multivariate extreme value problems. The Principal Investigator on the grant is Jennifer Wadsworth and the postdoc position will be based in Lancaster.
Your role would be to work, both independently and together with the PI and collaborators, on statistical methodology for, and applications of, multivariate extreme value theory. You will publish and disseminate results via journal articles, conference presentations and open source computer code.
You should have completed, or be close to completing a PhD in statistics, data science or a closely related discipline. You should have a good record of research in terms of submitted and draft publications, and be able to demonstrate strong programming skills (e.g. R, Matlab, C++). Experience with multivariate extreme value theory is essential, whilst experience with the so-called “geometric approach” to multivariate extremes would be highly advantageous.
The position is available from September 1st 2026, and will run for a maximum of 5 months. Interviews will be carried out shortly after the closing date. We welcome applications from people in all diversity groups. Interested candidates are encouraged to informally contact Dr Jennifer Wadsworth (j.wadsworth@lancaster.ac.uk) if they have any queries about the position.
Please note: unless specified otherwise in the advert, all advertised roles are UK based.
Senior Research Associate in Statistics employer: Lancaster University
The School of Mathematical Sciences at Lancaster University is an exceptional employer, offering a collaborative and innovative work environment that fosters academic growth and research excellence. With a strong emphasis on diversity and inclusion, employees benefit from access to cutting-edge resources, opportunities for professional development, and the chance to contribute to impactful research in a picturesque campus setting. Join us in advancing statistical methodology while enjoying a supportive community that values your contributions.
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We think you need these skills to ace Senior Research Associate in Statistics
Some tips for your application 🫡
Highlight Your Data Projects:When applying for a temporary data science role at Lancaster University, make sure to showcase any relevant projects you've worked on. Whether it's a personal project, an academic undertaking, or contributions to an open-source initiative, detailing these experiences can really set you apart and demonstrate your practical skills.
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Show Your Flexibility:Since this is a temporary role, it's important to convey your adaptability and willingness to learn. In your cover letter to Lancaster University, emphasise how quickly you can get up to speed with new tools or projects. Highlight any previous experiences where you've had to adjust to new environments or challenges.
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How to prepare for a job interview at Lancaster University
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You might face technical questions on tools relevant to data science, like Python, R, or SQL. Prepare to solve a problem live—perhaps they'll ask you to write a simple query or code snippet. It’s cool to talk about them, but we need to show we can do it in practice, especially in a temporary role where quick results matter.
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Since this is a temporary position, emphasise your ability to learn quickly and adapt to new tools or workflows. Share examples of how you've thrived in fast-paced environments before, and how you can hit the ground running at Lancaster University.
✨Prepare a Portfolio of Your Work
Bring your portfolio to the table—showcase projects where you've leveraged data science techniques to solve problems. Whether it’s a GitHub repository or a set of case studies, having tangible examples of your work will help you stand out and show what you bring to the team at Lancaster University.
