SB Boogert

This PhD project at The University of Manchester focuses on accelerating neutral particle transport simulations using artificial intelligence. State-of-the-art Monte Carlo simulations are essential for modeling nuclear reactors (both fission and fusion) and radiation shielding, but they are among the most computationally demanding tasks in science. The project aims to leverage advances in AI and machine learning to significantly improve the efficiency and accuracy of these simulations, with potential applications in nuclear physics, medical physics, and space science. As part of the Department of Physics and Astronomy, you will join a vibrant research environment and work under the supervision of Professor SB Boogert. The research will involve developing and applying novel AI-driven algorithms to complex stochastic processes, integrating computational mathematics and engineering mathematics with cutting-edge physics research. You will have the opportunity to collaborate with interdisciplinary teams and contribute to advancements in both fundamental science and practical engineering applications. Applicants should have a strong background in physics, mathematics, computer science, or a related field, with experience in computational methods and programming. Familiarity with machine learning, artificial intelligence, or Monte Carlo techniques is highly desirable. The project is suitable for candidates interested in pushing the boundaries of computational physics and exploring innovative solutions to real-world scientific challenges. The application deadline is 5 December 2025. For more information and to apply, visit the project page on FindAPhD or The University of Manchester's postgraduate application portal.

The University of Manchester is offering a fully funded PhD position titled 'Accelerating Neutral Particle Transport with AI' within the Department of Physics and Astronomy. This research opportunity is part of the UKRI AI CDT in Decision Making for Complex Systems, a TechExpert pilot program, and is supported by an industry partner, the UK Atomic Energy Authority (UKAEA). The project focuses on leveraging artificial intelligence and machine learning to significantly accelerate Monte Carlo simulations used in nuclear (fission and fusion) reactors and shielding, which are among the most computationally demanding simulations in science. The successful candidate will investigate advanced AI/ML techniques to improve simulation speed while ensuring accuracy and avoiding bias or artifacts. This work will enable rapid design and optimization of reactors, accelerators, and nuclear installations, contributing to the UK's national capability in artificial intelligence and its applications in science and engineering. The research areas include Artificial Intelligence, Machine Learning, Computational Mathematics, Engineering Mathematics, Stochastic Processes, Computational Physics, Medical Physics, Nuclear Physics, Particle Physics, and Space Science. Applicants should have a strong academic background in Physics, Nuclear Engineering, Computer Science, or Engineering. Required application documents include final and interim transcripts, CV, a supporting statement outlining motivation and relevant experience, and contact details for two referees with official email addresses. An English Language Certificate is required if applicable. The selection process is committed to equality, diversity, and inclusion, welcoming applicants from all backgrounds and those returning from career breaks. Flexible working arrangements and part-time study options are available. The position is fully funded for four years, with a stipend of £31,000 for students eligible for home fee status. The project start date is September 2026. The application deadline is April 10, 2026. Applicants are encouraged to contact the project supervisor before applying and must submit all required documents via the University of Manchester application portal, selecting 'PhD in Artificial Intelligence'. Incomplete applications will not be considered. This opportunity is ideal for candidates passionate about uncertainty quantification, decision making with humans in the loop, and decision making for machine learning systems in science and engineering. Join a vibrant research community dedicated to innovation, diversity, and societal impact.