AI-Powered Reliability & Optimisation for Offshore Cables

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

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