Research Associate (AI-Enhanced Predictive Modelling and Optimisation)

At Teesside University, we are ambitious in our goals, vision and aspirations. We challenge expectations and push the boundaries of what is possible. From transforming our campus through a £300m investment and capital development programme, to creating outstanding, innovative academic programmes, our work is always relevant and purpose-driven.

Applications are invited for a Research Associate to join an externally funded collaborative research project focused on developing AI-enhanced methods for the reliability assessment and optimisation of offshore cable systems, cable protection systems (CPS), and associated components.

The Research Associate will develop and validate AI predictive models, uncertainty quantification methods, and optimisation algorithms for the reliability assessment and optimisation of offshore cable systems, cable protection systems (CPS), and associated components. The postholder will process engineering simulation data, develop AI-based predictive and optimisation methods, and support their integration and validation within an engineering software platform, working closely with university researchers and industrial partners.

The successful candidate will contribute to:

• offshore cable modelling and engineering simulation;
• engineering data processing and analysis;
• AI and machine-learning model development;
• uncertainty quantification and reliability assessment; and
• AI-driven optimisation, model validation, and software integration. 

For more information, please see the Job Description and Person Specification at  https://www.tees.ac.uk/researchjobs/

This position is "Subject to having the grant agreement in place". 

Please be advised that due to the minimum salary thresholds imposed by the UKVI, this post may qualify for University sponsorship under the Skilled Worker visa route.

If you are shortlisted, your interview will take place via Microsoft Teams.Please note that the University may ask you to participate in a number of selection activities as part of the recruitment process for this vacancy.

Apply now if you would like to join us.

Closing date 16 September 2026

Interview date: 2 October 2026 

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