Research Associate

Are you a passionate researcher with expertise in CFD, nuclear thermal hydraulics, or machine learning?

Join a groundbreaking project at the Collaborative Computational Project in Nuclear Thermal Hydraulics (CCP-NTH), a UK research consortium funded by the research councils (visit https://ccpnth.ac.uk/ for more).

The intricate, multi-scale nature of nuclear reactor systems poses significant challenges for accurate modelling. While fully resolved CFD simulations remain computationally infeasible, under-resolved and coarse-grid models can provide valuable insights, especially when coupled with system-level approaches and localized high-fidelity simulations. This project seeks to exploit the potential of AI/ML to improve the accuracy and efficiency of these simplified models. By carefully evaluating various methodologies, we aim to develop advanced modelling techniques that incorporate new physical insights and leverage emerging computational technologies.

You should hold a PhD (or be nearing completion) in computational fluid dynamics, nuclear thermal hydraulics, or machine learning, or have equivalent experience. Candidates should have a strong background in one or more of the following areas: nuclear thermal hydraulics, programming, turbulence, CFD, AI/machine learning, and flow physics.

The University of Sheffield is a remarkable place to work. Our people are at the heart of everything we do. Their diverse backgrounds, abilities and beliefs make Sheffield a world-class university.

We offer a fantastic range of benefits including a highly competitive annual leave entitlement (with the ability to purchase more), a generous pensions scheme, flexible working opportunities, a commitment to your development and wellbeing, a wide range of retail discounts, and much more.

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