Research Fellow in Machine Learning Assisted Choice Modelling

University of Leeds
Leeds, West Yorkshire
Last month
Applications closed

Related Jobs

View all jobs
Featured

Engineering Manager - AI Defence Product (dominAI)

Hadean London
Hybrid Clearance Required

Research Fellow in Artificial Intelligence and Machine Learning for Scientific Discovery

The University of Manchester Manchester
£48,337 – £59,390 pa Hybrid

Research Fellow in AI/ML (Artificial Intelligence / Machine Learning)

University of Hertfordshire Hatfield
£39,560 – £46,970 pa On-site Clearance Required

Research Fellow in Medical Image Analysis & Machine Learning

UCL London
£46,261 – £48,679 pa Contract

Power Electronics Researcher - AI & Machine Learning

Rise Technical Recruitment Solihull
£45,000 – £62,000 pa Permanent

PhD Studentship: Sovereign Agentic AI: Distilling Small Language Model Agents for Local Deployment

University of Exeter Exeter
£21,805 pa On-site
Job Type
Contract
Work Pattern
Full-time
Posted
15 Sep 2026 (Last month)

Working Time

100% FTE - 37.5 hours per week

Post Type

Full Time

Contract Type

Fixed Term (12 months to complete specific time limited work.)

This role will be based on the university campus with scope for it to be undertaken in a hybrid manner. We are also open to discussing flexible working arrangements.

The University of Leeds is one of the top 80 universities in the world. We have a truly global community, with more than 39,000 students from 170 different countries and over 9,000 staff of 100 different nationalities. Established in 1904, we have a strong tradition of academic excellence, reflected in first-class student education, along with world-leading research that has a real impact around the globe.

Do you have expertise in discrete choice modelling, behavioural data science or machine learning? Are you interested in developing the next generation of AI tools that accelerate scientific discovery while maintaining behavioural realism and interpretability?

You will join the Choice Modelling Centre to develop AI-assisted methods for specifying, estimating and validating discrete choice models. The project aims to bring together ideas from machine learning, econometric choice modelling and mathematical psychology. You will investigate how large language models, optimisation algorithms and automated model search methods can support researchers in constructing robust behavioural models without replacing scientific judgement. The emphasis is on interpretable AI processes that complements econometric theory and domain expertise.

The successful candidate will lead methodological research, develop open-source software, publish in leading journals, and collaborate with researchers across transport, health and environmental applications.

Visa Eligibility

Please note that this post may be suitable for sponsorship under the Skilled Worker visa route but first-time applicants might need to qualify for salary concessions. For more information please visit: www.gov.uk/skilled-worker-visa.

For research and academic posts, we will consider eligibility under the Global Talent visa. For more information please visit: https://www.gov.uk/global-talent

What we offer in return

  • 26 days holiday plus approx.16 Bank Holidays/days that the University is closed by custom (including Christmas) – That’s 42 days a year!
  • Generous pension scheme options plus life assurance
  • Health and Wellbeing: Discounted staff membership options at The Edge, our state-of-the-art Campus gym, with a pool, sauna, climbing wall, cycle circuit, and sports halls.
  • Personal Development: Access to courses run by our Organisational Development & Professional Learning team.
  • Access to on-site childcare, shopping discounts and travel schemes are also available.

And much more!

To explore the post further or for any queries you may have, please contact:

Professor Stephane Hess, Professor of Choice Modelling

Email:

Further Details:
Candidate Brief

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.