[School of Natural Sciences PhD Scholarships] Machine-learning methods for analysing quantum turbulence in superfluid helium

About the project

About the Project Quantum turbulence is a remarkable form of fluid motion that occurs in superfluids. Unlike turbulence in ordinary fluids, quantum turbulence is sustained by a dynamic tangle of quantized vortices, each carrying a fixed amount of circulation. Understanding how these vortices move, interact and transfer energy is an important challenge in quantum-fluid research, with connections to fundamental fluid dynamics and other systems containing quantized vortices. Our group has recently performed experiments using a novel optical visualisation setup producing videos of small tracer particles moving through turbulent superfluid helium. These measurements offer a new opportunity to investigate how particles interact with the underlying vortex tangle and thermal excitations present at finite temperatures. However, extracting quantitative information from the videos presents an interesting computational challenge. The images can have low contrast, fluctuating brightness and uneven background. These limitations stem from the small size of particles and constrains on the intensity of illuminated light that can be brought into superfluid helium maintained at millikelvin temperatures. Individual particles must first be detected reliably before their positions can be linked between successive frames to reconstruct time-dependent trajectories. The aim of this PhD project is to develop a robust framework for a systematic analysis of these experimental videos. The student will investigate methods for enhancing the visibility of tracer particles, identifying their positions and linking detections across many frames. The resulting trajectories will then be used to study different types of particle motion. Some particles move coherently through the fluid, whereas others display irregular motion resembling a chaotic random walk as a result of their interaction with quantized vortices. Machine-learning methods will be applied to distinguish between these characteristic behaviours and to identify patterns within large collections of trajectories. The analysis will provide new information about the dynamics of quantum turbulence at temperatures below 1 K and the interaction between particles and quantized vortices. The project will provide training in scientific programming, digital image and video analysis, particle tracking, machine learning, statistical analysis and the physical interpretation of experimental data. No prior expertise in all of these areas is expected. The student will work closely with a supervisory team who will provide training in the relevant computational techniques, experimental context and physics of superfluid helium. There will also be opportunities to collaborate with other groups in the Department that have expertise in applying machine learning to particle physics, biophysics and astrophysics. This combination of fundamental physics and advanced computation will allow the student to develop a broad and highly transferable research skill set. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Natural Sciences Scholarships . If you don’t already have an applicant account, please follow the instructions here . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing your motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree (or international equivalent) in Physics, Mathematics, Engineering, Computer Science or Data Science OR any upper-second class (2:1) honours degree and a Master’s degree at merit (or international equivalent) in Physics, Mathematics, Engineering, Computer Science or Data Science. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoNS

Research areas