Summer research placements - summer 2025
On this webpage you can learn about and apply for summer placement opportunities in summer 2025.
Placements are made available across the partners comprising the CDT: Bangor University, University of Bristol, University of Cambridge, Imperial College London and The Open University. Where possible we will run placements synchronously (or as close as possible) in order to provide students with a cohort experience.
Please read the FAQs below for further information. If your question is not answered below, please contact Dr Jon Tate.
Current summer research placement projects
Quantum Algorithms for Plasma Physics
Computer simulations are an essential tool for advancing our understanding of Plasma Physics. The complexity of plasmas mean that these simulations are usually very computationally intensive. Quantum computers offer the potential to solve many problems significantly faster than can currently be done on classical computers. However, it is not yet clear how quantum computers can speed up the solving of a class of problems that commonly occur in Plasma Physics, namely, the numerical solution of partial differential equations. This project will focus on the development of a quantum computing algorithm for solving the Vlasov-Fokker-Planck equation (an equation which governs the kinetic behaviour of plasmas), using a technique known as Carleman linearization. The project will involve some analytic work as well as the development of both classical and quantum computing models.
Supervisor: Dr Brian Appelbe, Department of Physics, Imperial College London
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Hydrogen/Deuterium Permeability in Oxidised Plasma-facing Materials
Achieving the ambitious objectives of nuclear fusion power requires addressing important material challenges, including the understanding of chemical redox reactions and their impact on power plant integrity/maintenance, and tritium retention and recovery from waste. Previous research has identified risks from the oxidation of plasma-facing materials, with a significant impact on the operation, maintenance, and safety of a commercial fusion reactor. Experimental research is currently being performed in the Materials Degradation and Sustainability Group, delivering new insights into the oxidation kinetics and nature of the oxidation products of plasma-facing alloys. This project aims to complement this experimental work, by developing an electrochemical permeation procedure that can provide new insights into the interaction of hydrogen-isotope with the oxide layer of plasma-facing materials. Additional electrochemical hydrogen charging and thermal desorption spectroscopy (TDS) will also be used to study the synergistic effects of hydrogen charging/release and oxidation process.
Supervisor: Dr Livia Cupertino Malheiros, Department of Civil and Environmental Engineering, Imperial College London
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Critical Experiments to Understand Oxidation Behaviour of Plasma-facing W-based Materials
Tungsten is one of the main plasma-facing materials because of its excellent high thermal conductivity, low erosion rate, low fuel retention, and low neutron activation. However, the oxidation of tungsten occurs at a very low partial pressure of oxygen, causing significant loss of first wall materials and potential dispersion of tungsten oxide in the form of sublimated gas molecules or dust spalled off from the oxide scale. This can in turn have a detrimental impact on the operation and the sustainability of the reactor. Recent modelling work has shown that one of the tungsten oxide phases (WO2) could act as a tritium barrier [1], which will impact the tritium retention of tungsten-based components. Therefore, it is important to understand the microstructure of the tungsten oxide scale (oxide phase formed and morphology) and the effect of alloying elements on metal oxide nucleation and growth processes under controlled environments and temperature conditions. The Centre for Infrastructure Materials has acquired state-of-the-art simultaneous thermal analysis (STA) equipment to gain new insights into materials oxidation. In this project, the student will conduct oxidation experiments using the STA with post-oxidation analysis using scanning electron microscopy (SEM) and Raman spectroscopy on W and W-based shielding materials (WCx, WBx).
Supervisor: Dr Livia Cupertino Malheiros, Department of Civil and Environmental Engineering, Imperial College London
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Using Machine Learning to Study Radiation Damage in SiC Fusion Reactor Blankets
High energy neutrons produced by fusion reactions cause severe damage to materials. They can knock atoms from their lattice positions, generating a vacancy in one lattice site and an interstitial defect elsewhere, and can cause transmutation events to occur, where an atom is transformed into a different element type. These defects can then migrate, cluster, and interact affecting the properties of these critical components. SiC/SiC composites are used in Kyoto Fusioneering’s SCYLLA and General Atomic’s GAMBL blanket designs where they are exposed to this challenging environment. SiC is also being studied by UKAEA Materials Science and Engineering. Understanding the behaviour of both of these kinds of irradiation defects in neutron irradiated SiC is an active area of research. In particular, little is known about the behaviour of Al produced from transmutation in neutron irradiated SiC.
In this project, the student will use a Universal Machine Learning Interatomic Potential (UMLIP) on a high-performance computing system to produce a first-of-its-kind study of the evolution of aluminium atoms produced by transmutation in irradiated SiC. UMLIPs are a state-of-the-art potential that enable accuracy approaching first-principles approaches with orders of magnitude lower computational cost with the advantage that they cover much of the periodic table. The student will first benchmark results against previous first principles calculations, and then conduct a larger scale simulation of irradiation damage evolution with Al transmutation products. The results of this study will help predict how this changes the performance of SiC composites in a breeder blanket. These computational results will also help interpret experimental work being undertaken at UKAEA.
Supervisor: Dr Colleen Reynolds, Department of Materials, Imperial College London
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Using Machine Learning to Improve the Accuracy of Atomic Scale Experiments
Atom probe tomography (APT) is a microscopy technique that enables us to see individual atoms in a material. By removing atoms one by one from a sharp needle-shaped specimen (typically 50 nm in width), a reconstruction can be made by placing atoms into their predicted sites in three dimensions. However, when doing this, some information is lost. Only around 50% of the atoms are detected, and the error in the detected position is around 0.3 nm. This limitation has been accepted, as APT is the only 3D atomic scale mass spectroscopy technique. However, more recent advances in the field of machine learning, most notably in graph neural networks, have opened up a new avenue to reduce the inherent errors with APT. Universal machine learning interatomic potentials (uMLIPs), first explored by Google with their GNoME project, have been able to approach near-quantum level accuracy at vastly cheaper computational costs. This reduction in cost presents an opportunity to use the MLIPs in the APT data and move the atoms to more energetically favourable sites that should correspond more closely to the real positions. This UROP project is funded by UKAEA through the NEURONE project to develop new high-temperature advanced reduced activation ferritic martensitic (ARAFM) steels for fusion blankets and aims to apply some MLIPs, such as MACE, to already collected APT datasets to improve spatial resolution. There will also be the opportunity to explore collaborations with the atom probe group at the University of Oxford.
Supervisor: Dr Ryan Stroud, Department of Materials, Imperial College London
FAQs
What is the duration of a summer placement?
We intend for summer placements to last around 8-10 weeks in the months of June, July, August and September. All placements will conclude before the beginning of October. Exact timeframes will be agreed when an offer is made to the student.
Will the summer placement be funded?
It is our policy to provide summer placement students with the current National (or London) Living Wage. Exact funding will be agreed when an offer is made to the student.
What are the eligibility criteria?
The student should be registered to an undergraduate programme at UK university studying a degree with some relevance to the placement they are applying for. Ideally the student will be in their second or third year of said degree.
There are no nationality or residency requirements.
Students should be willing to relocate to the location of the placement where applicable.
Can I apply for more than one project?
Yes, you are allowed to apply for multiple projects.
What work will I do?
Nuclear is a broad subject so it is difficult to be prescriptive about the work undertaken. The project descriptions will provide information here, and you can always contact the project supervisor for further information.
The expectation is that at the end of your placement you will deliver a presentation of the work undertaken to a CDT co-Director, your project supervisor and wider research group, and other members of your summer placement cohort.
How do I apply?
Students that would like to apply for a summer placement should send the following documents to Dr Jon Tate with the title of the project in the e-mail title:
- CV/Resume
- Cover Letter detailing your motivation
- Contact details of one academic reference
What is involved in the application process?
Your application will be reviewed by the CDT Director Dr Mark Wenman, the CDT Manager Dr Jon Tate, and the supervisor for the project.
Students that pass this first stage will be invited to a short MS Teams interview where they can be expected to be asked questions about their motivation for the summer placement, their career aspirations, and some technical questions about the project area.
A shortlist of up to three students will be created, and we will work down that shortlist until the successful student confirms they would like to accept the offer of the placement.
