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Research Associate in Numerical Analysis and Probabilistic AI

The University of Manchester - Department of Mathematics

Location: Manchester
Salary: £37,694 to £46,049 per annum
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 9th December 2025
Closes: 12th January 2026
Job Ref: SAE-030345

Job reference: SAE-030345
Salary: £37,694 - £46,049 per annum according to relevant experience
Faculty/Organisational Unit: Science and Engineering
Location: Oxford Road
Employment type: Fixed Term
Division/Team: Department of Mathematics
Hours Per Week: Full time
Closing date (DD/MM/YYYY): 12/01/2026
Contract Duration: Fixed term up to 36 Months
School/Directorate: School of Natural Sciences

BACKGROUND

We invite applications for a Post Doctoral Research Associate position to join the EPSRC-funded Probabilistic (ProbAI) Hub. The vision of the ProbAI Hub is to develop a world-leading, diverse and UK-wide research programme in probabilistic AI, developing the next generation of mathematically principled, scalable and uncertainty-aware AI algorithms. This will be achieved by bringing together world-leading researchers from a range of underpinning disciplines, including probability and statistics, applied mathematics and numerical analysis, and other areas of computational science. The Hub’s research activities are centered around five key themes. For more information, see:

The recruited PDRA will join a large existing inter-disciplinary research group comprising academic staff, PDRAs and PhD students from the Universities of Lancaster, Bristol, Cambridge, Edinburgh, Manchester and Warwick.

Job Summary:

This numerical analysis-focused PDRA position, will be based at the University of Manchester, but co-supervised by lead researchers at both the University of Manchester (Professor Catherine Powell) and the University Edinburgh (Dr. Aretha Teckentrup). Funding will be available to visit the University of Edinburgh and to attend Hub-sponsored events, including problem solving weeks, conferences and internal retreats.

We are seeking a candidate with a strong background in some aspect of numerical analysis for PDEs and an interest in scientific machine learning and probabilistic methods, who enjoys working in collaborative inter-disciplinary settings, who will contribute to the Hub’s research efforts under the following themes:

  • Structure-constrained and Informed AI
  • Probabilistic and Uncertainty-aware Methods for Trustworthy and Green AI

A range of potential projects are available, depending on the level of expertise and background of the successful candidate. A representative list of possible topics is given in the Further Particulars document below.

Applicants are encouraged to include in their application a covering letter or statement outlining how they feel they can contribute to the research of the Hub (as outlined in the Further Particulars document) and what sort of projects they are most interested in. This should be collated with a CV as one pdf document under 1 MB when upload.

Interviews are provisionally scheduled to take place in the week commencing 26th January 2026.

As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

Our University is positive about flexible working – you can find out more here

Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.

Any CV’s submitted by a recruitment agency will be considered a gift.

Enquiries about the vacancy, shortlisting and interviews:

Name: Professor Catherine Powell

Email: Catherine.Powell@manchester.ac.uk

General enquiries:

Email: People.recruitment@manchester.ac.uk

Technical support:

https://jobseekersupport.jobtrain.co.uk/support/home

This vacancy will close for applications at midnight on the closing date.

Further particulars including job description and person specification are available on the University of Manchester website - click on the 'Apply' button above to find out more.

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