PhD Studentship: Stochastic vs Deterministic vs AI Accelerated Methods for HTGR Design

The University of Manchester - Mechanical and Aerospace Engineering

Qualification Type:PhDLocation:ManchesterFunding for:UK StudentsFunding amount:£20,780 annual tax-free stipend set at the UKRI rate and tuition fees will be paidHours:Full TimePlaced On:3rd March 2026Closes:30th March 2026

Application deadline: 30/04/2026

Research theme: Nuclear Engineering

How to apply: https://uom.link/pgr-apply-2425

This 3.5-year PhD project is fully funded; home students are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.

We recommend that you apply early as the advert may be removed before the deadline.

High-temperature gas-cooled reactors (HTGRs) are of significant interest to the UK due to their ability to deliver high-temperature, low-carbon energy for applications beyond electricity generation, and building on the UK’s extensive experience of gas-cooled reactor operation. HMG has selected the High Temperature Gas-cooled Reactor (HTGR) as the most credible Advanced Modular Reactor (AMR) technology.

Achieving improved performance requires accurate, high-fidelity modelling to reliably predict power distributions and ensure fuel operates within design limits.

The aim of this project is to establish a robust, validated framework for neutronics and thermal analysis of prismatic HTGR cores, integrating Monte Carlo simulation, deterministic methods, and AI/ML-assisted techniques. The study seeks to improve predictive accuracy for key performance and safety parameters while addressing the challenges associated with cross-section generation and homogenisation in HTGRs.

The project will aim to:

• Quantify the limitations of conventional deterministic HTGR analysis, particularly those arising from cross-section homogenisation and energy group structure.
• Investigate the use of AI/ML algorithms to predict or generate cross sections, enabling deterministic solvers to better capture strong heterogeneities and flux gradients.
• Provide insight on the appropriate use of Monte Carlo, deterministic, and AI-accelerated approaches for HTGR design, safety assessment, and operational analysis.
• Develop and validate a multiscale thermal solver tailored to prismatic HTGRs, capable of resolving heat transfer at the TRISO particle, fuel compact, and graphite block levels, and tightly coupled to a Monte Carlo neutronics solver.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

To apply, please contact the main supervisor, Dr Olga Negri - olga.negri@manchester.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.