PhD Studentship in Industrial Engineering

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.

In your application, please refer to Polytechnicpositions.com