PhD Studentship in Industrial Engineering

University of Nottingham

Institution: University of Nottingham – Mechanical and Aerospace Systems Research Group

Location: UK

Reference: ENG305

Closing Date: Wednesday, 29 April 2026

Funding: Fully funded – UK Home fees + tax-free stipend of £24,000/year for 4 years

---

Project Overview

This Industrial Doctoral Landscape Award, in partnership with Siemens Digital Industry Software, focuses on advancing Computational Fluid Dynamics (CFD) for industrial applications using machine learning (ML). Key objectives:

• Enhance boundary layer modelling in under-resolved aerodynamic simulations.
• Develop ML architectures trained on high-fidelity CFD datasets.
• Integrate ML-based boundary layer models into open-source finite volume CFD codes.
• Conduct a 3-month industrial placement at Siemens for hands-on experience.

The PhD combines fundamental fluid mechanics with modern data-driven methods for direct industrial impact.

---

Candidate Requirements

Essential:

• High 2:1 (preferably 1st class) honours degree in Mechanical/Aerospace Engineering or related discipline
• Strong understanding of numerical methods and fluid mechanics
• Experience with scientific programming and data analysis (Python, MATLAB, Julia, C/C++ etc.)
• Ability to work independently and collaboratively

Desirable:

• Prior experience with CFD applications
• Understanding of meshing requirements for aerodynamic simulations
• Experience with machine learning or data-driven modelling techniques

Note: Studentship is limited to UK (home fees) applicants.

---

Application Instructions

Submit the following documents to Hadrian.moran@nottingham.ac.uk:

• CV
• Cover letter
• Academic transcripts

For informal enquiries, contact Dr Stephen Ambrose.

---

This PhD is ideal for candidates interested in CFD, fluid mechanics, and ML for industrial engineering applications.

Apply