PhD Studentship: Machine Learning â Enhanced Boundary Layer Modelling for Industrial CFD in partnership with Siemens Digital Industry Software (ENG305)

Location: Mechanical and Aerospace Systems Research Group, Faculty of Engineering, University of Nottingham

Funding: UK Home fees + tax-free stipend of £24,000 p.a. for 4 years

Applications are invited for a fully funded Industrial Doctoral Landscape Award in partnership with Siemens Digital Industry Software, focused on advancing the next generation of industrial Computational Fluid Dynamics (CFD). The project investigates how machine learning (ML) can be used to enhance the modelling of boundary layers in industrial CFD simulations, where complex geometries and computational constraints limit near-wall resolution. This PhD offers the opportunity to conduct cutting-edge research with direct industrial impact, combining fundamental fluid mechanics with modern data-driven techniques.

The successful candidate will join a supportive team of over 50 researchers, technicians and academics within the Mechanical and Aerospace Systems Research Group, and will have the opportunity to apply their research during a placement within Siemens Digital Industry Software.

Project Overview

The project focuses on developing and integrating ML techniques to enhance wall treatments for under-resolved boundary layers in aerodynamic simulations for industrial applications. In many industrial settings, complex geometries and restricted computational resources make it impractical to generate sufficiently refined near-wall meshes, limiting the accuracy of conventional boundary layer modelling approaches.

During the PhD, the student will curate an archive of high-fidelity simulation data spanning a range of representative application areas, which will be used to train and assess boundary layer neural network models. The student will develop and evaluate suitable ML architectures, analysing the trade-offs between different modelling strategies and levels of fidelity. By the end of the project, the student will demonstrate the integration of ML-based boundary layer models within an open-source finite volume CFD code and quantify their performance relative to current pragmatic industrial approaches.

The successful candidate will spend at least 3 months during the PhD based within Siemens Digital Industry Software, receiving joint supervision and training from both academic and industrial researchers, and gaining direct exposure to industrial CFD workflows and software development practices.

Candidate Requirements

We are seeking an enthusiastic, self-motivated researcher with a rigorous approach to problem-solving. Applicants should have, or be expected to gain, a high 2:1 (preferably 1st class) honours degree in Mechanical or Aerospace Engineering, or a related discipline with substantial background in fluid mechanics.

Essential skills:

•        Strong knowledge of numerical methods and fluid mechanics

•        Experience with scientific programming and data analysis (e.g. Python, Julia, MATLAB, C/C++, or similar)

•        Ability to work independently and as part of a collaborative research team

Desirable skills / experience:

•        Experience of applying CFD to a complex problem

•        Appreciation of meshing requirements for aerodynamic simulations

•        Experience with machine learning or data-driven modelling techniques

Funding

This studentship covers UK home tuition fees and provides a tax-free stipend of £24,000 per year for 4 years. Please note that, due to funding restrictions, this studentship is only available to UK (home fees) citizens.

Application Process

Informal enquiries may be addressed to:

Dr Stephen Ambrose – Stephen.Ambrose3@nottingham.ac.uk   

Interested candidates should submit the following documents:

•        Curriculum Vitae (CV)

•        Cover letter

•        Academic transcripts

Applications should be sent to: Hadrian.moran@nottingham.ac.uk  

Closing Date: 29 Apr 2026
Category: Studentships