Physics Departmental Summer Placement Scheme 2026

The main aims of the project are:

-Hands on experience with a low temperature lab group, learning about the operation of dilution refrigerators.

-Learn and practise, 3D CAD design and 3D printing.

-Rasberry Pi and python coding to simulate the guts of the fridge.

-Put it all together and have a piece of demonstration equipment that should last Holloway generations of physicists.

This is an on-campus only placement and any academic year can apply.

Thermoelectric sulphides have potential to recover waste heat back into useful power. This project will look to understand the local order in the material lilianite by modelling synchrotron x-ray diffuse scattering.

1. Preferred year 2+
2. On campus/hybrid/remote all possible.
3. prerequisite skills : python programming.
4. Timeframe. Project ideally needs to conclude by August when Dr Voneshen expects to start paternity leave for 3 months.

Neutron stars are the densest compact objects in the Universe. Due to their charged crusts and rapid rotations, neutron stars radiate electromagnetic pulses in nearly perfect periods and are therefore also known as pulsars. Rotation/pulsar glitches refer to the sudden changes in the rotations, and one of the main mechanisms is the angular momentum exchange between the crust and interior, composed of superfluid neutrons. This project will provide new insights into neutron star microphysics by adapting the two-dimensional superfluid point-vortex model to investigate how to trigger large glitches, which remain inconsistent with observation estimations in the current literature. Our findings aim to bridge the gap between microscopic neutron star behaviour and large-scale glitch models, potentially improving understanding and predictive capabilities for pulsar observations.

• Any pre-requisite skills: Basic coding skills in Julia, Python or MATLAB.
• Preferred academic year/degree title: 2nd year at least.
• On campus/Hybrid/Remote: Hybrid.
• Preferred timeframe (flexible, must start in July, etc): Flexible

The Ising model is the archetype of systems that exhibit a phase transition and has inspired generations of physicists. This summer project approaches the statistical mechanics and computational physics of the Ising model.

• Final year student preferred with theoretical research experience

1 Background
In this project, you’ll learn about the physics behind neutrino oscillations, state of- the art machine learning methods for analyzing data, and how to put machine learning to the test to make sure it delivers accurate and well-supported physics. Neutrinos are among the least well understood particles in the standard model due to both their “ghost-like” ability to pass through most matter and their unique ability to transform as they travel. This transformation property or “oscillation” [1] is of particular interest to physicists since it has the possibility of explaining differences between matter and anti-matter; understanding this is key to our explanation of why we see an excess of matter vs anti-matter (CP violation).

The next generation of long-baseline neutrino experiments aim to observe this property by firing a beams of νμ and ¯νμ at detectors hundreds of kilometres away. Since neutrinos oscillate, a fraction of these neutrinos will change flavour whilst traveling. These neutrinos are measured using some of the largest detectors ever built in particle physics, Hyper-Kamiokande [2] and DUNE [3].

One of the key issues faced by these experiments is the time taken to actually run these data analyses as they need to perform tens of billions of calculations to create reliable results. One approach being tested at RHUL is ”simulation based inference” (SBI) [4]. Here, machine learning is used to create a surrogate algorithm for the expensive calculations. Currently we don’t know how well our machine learning algorithm behaves when the the data from our detector is really extreme in so-called ”nightmare” scenarios. This project will involve testing this behaviour across a wide variety of physics scenarios and analysing the results. In addition, there will also be the opportunity to present this work both to the neutrino/dark matter group and to a wider physics audience within
the field.

2 Work Plan
• Week 1-2: Develop basic understanding of machine learning techniques applied, Markov chain Monte Carlo and neutrino oscillations.

• Week 3-5: Implement testing code for pull studies and produce some initial
• Week 5-6: Project write up. Additionally there will be an opportunity to present this work to a wider physics group.

3 Possible Extensions
This project has several possible extensions
• How well does this work with model-free fits on far larger parameter
spaces?

References
[1] G Bellini, L Ludhova, Giorgia Ranucci, and FL Villante. Neutrino oscillations. Advances in High Energy Physics, 2014(1):191960, 2014.
[2] Ke Abe, Ke Abe, H Aihara, et al. Hyper-kamiokande design report. arXiv preprint arXiv:1805.04163, 2018.
[3] B Abi, R Acciarri, et al. Deep underground neutrino experiment (dune), far detector technical design report, volume ii: Dune physics. arXiv preprint arXiv:2002.03005, 2020.
[4] Michael Deistler, Jan Boelts, et al. Simulation-based inference: A practical guide, 2025.

Radio pulsars, as their name indicates, emit pulsed radio emission that can be detected with non-imaging telescopes. These instruments do not provide "images" of pulsars, and hence, we lack information about their precise location. However, the long-term tracking of their pulse time of arrivals, i.e., their timing, enables us to compute their position with very good precision. During this summer project, the student will analyze radio pulsar data from an ongoing observing campaign aiming at narrowing down the position of two newly discovered pulsars. 

• Pre-requisite: some coding basics, terminal skills
• Preferred academic year: None
• On campus
• Timeframe: flexible

Structural disorder plays a key role in the behaviour of quantum materials but, until now, it has been difficult to determine defect structures using diffraction techniques. This project will apply a novel approach using the diffuse scattering of x-rays away from Bragg peaks to determine the defect structures in spin ice materials. Experiments will be performed using the Xcalibur single-crystal x-ray diffractometer in the X-ray Lab, and modelling will be carried out using the instrumental software and using Python.

• Any pre-requisite skills: Python
• Preferred academic year/degree title: 2+
• On campus/Hybrid/Remote: On campus
• Preferred timeframe (flexible, must start in July, etc): June - July

Quantum computing is an important future industry. While quantum computers are now available commercially there is still much fundamental and applied research to be done. The project will create and study materials and structures for superconducting quantum computing using the thin-film deposition and characterisation tools available in SuperFab and the x-ray diffraction tools available in Physics. The aim is to characterise the quality of the materials and the resulting devices as a step towards finding ways to improve them. Students will work with the SuperFab technical staff as well as academic staff.

• Any pre-requisite skills: An interest in experimental work. Previous experience in x-ray work would be an advantage.
• Preferred academic year/degree title: Must have taken PH2710 The Solid State.
• On campus.
• Preferred timeframe: Flexible, but probably best to avoid working in August. 
• Number of students preferred, if project requires more than one: Two students could be taken

Analyse neutron diffraction and spectroscopy data of the order and low-energy excitations of NbFe2, which is located at the border of ferromagnetism but also shows an emerging spin-density wave. Order emerging from magnetic quantum criticality is a concept relevant for other areas of solid-state physics including unconventional and high-temperature superconductivity.

• On campus preferred
• Flexible start date
• Prefer end of 2nd or 3rd year
• Pre-requisite: Solid State Physics, Python programming
• Duration: 6 weeks

It is well known that qubits are not well thermalized down to the ~10 mK base temperature of a dilution cryostat. Another peculiar effect is the difference in the effective temperatures of the various energy levels of the qubit. This project will focus on developing a basic mathematical model of the qubit level population dynamics, and on assessing to what extent such a model can describe this behaviour, as well as which system parameters may be extracted from it.

• Any pre-requisite skills: Differential equations, quantum mechanics.
• Preferred academic year/degree title: at least 2nd year
• On campus/Hybrid/Remote: Hybrid
• Preferred timeframe (flexible, must start in July, etc): most of the work preferably should be finished before August.

Analysing (using python) datasets from NESO containing past data on sources of electricity feeding the national grid, various scenarios are explored on how to make the grid carbon free under various assumptions.

• On campus or hybrid
• Flexible start date
• Prefer 3rd year
• Pre-requisite: PH3040 (preferred, not required)
• Duration: 6 weeks

Cultural heritage research seeks to interpret and understand historical and cultural assets. One method through which knowledge can be gained about historically significant artifacts and locations is the use of particle accelerators. Accelerators provide a powerful tool for analysing and dating artifacts, and insights into their composition can reveal previously hidden information or aid in their preservation. It is crucial that the methods applied do not damage these assets and that meaningful data can be obtained from the analysis. Simulation plays a key role in understanding the capabilities and limitations of accelerator applications in cultural heritage research.

Beam Delivery Simulation is a software package developed at the Royal Holloway University of London to model particle accelerators. In this project, the student will investigate the feasibility of using BDSIM for applications in cultural heritage research. This will include modelling layered materials and utilising the pyg4ometry package to create custom geometric models with accurate cultural heritage artifact materials. BDSIM will then be used to simulate the interaction of particle beams with these materials, followed by analysis of the results.

• Any pre-requisite skills: Preferably on the particle physics stream. Must be 2nd year and above. Must have done the python module. 
• On campus/Hybrid/Remote: Hybrid
• Preferred timeframe (flexible, must start in July, etc): Flexible
• Duration 6 weeks