Research Associate (AI-Enhanced Predictive Modelling and Optimisation)

Teesside University; Tees Valley; On-site; GBP 32,000 - 42,000; Full time

Job summary

Teesside University invites applications for a Research Associate to join an externally funded project focused on AI-enhanced reliability assessment and optimisation of offshore cables and CPS components. You will develop AI predictive models, quantify uncertainties, and optimise methods for integration into an engineering software platform, working with researchers and industrial partners.

The role involves processing simulation data, developing ML workflows, and contributing to model

Qualifications

• Experience developing AI predictive models and ML workflows.
• Background in data processing and engineering data analysis.
• Familiarity with uncertainty quantification methods and reliability assessment.

Responsibilities

• Develop AI-based predictive models for offshore cable systems.
• Process engineering simulation data and validate AI methods.
• Contribute to AI-driven optimisation and model validation.
• Support integration of AI tools within an engineering software platform.
• Collaborate with researchers and industrial partners.

Skills

AI modelling

Machine learning

Data processing

Uncertainty quantification

Software integration

Job description

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
• 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.

Closing date 16 September 2026 Interview date: 2 October 2026