Current and future galaxy surveys aim to pin down the nature of the Dark Universe (dark energy and dark matter), constituting approximately 95% of our cosmos. In this project, you will investigate the use of cutting-edge Machine Learning algorithms, particularly the software COSMOPOWER, to accelerate the statistical inference of constraints on the Dark Universe from state-of-the-art surveys such as the European Space Agency’s mission Euclid.

Prerequisite: Python programming

Location: Hybrid and remote options available

A hands-on project to create a simple but hopefully fun imitation of how particle bunches behave in an accelerator to be displayed in the Accelerator Lab. This will require learning very basic concepts of accelerator physics and modelling them using LEDs and Raspberry Pi controls in a visual installation. Electronics skills and Python are an advantage, but not essential. Willingness to make things happen is! With help from the Group.

Prerequisite: none

Location: On-campus

Keywords: outreach, accelerators, electronics, mechanical CAD, Raspberry Pi, Python

This hands-on project aims to construct a pick and place machine for automated PCB assembly. It will involve controlling mover stages, automation and computer vision based on open-source hardware projects. Some practical skills in electronics and basic familiarity with Python would be a useful asset. With help from Luke Eddowes.

Prerequisite: none

Location: On-campus

Keywords: electronics, printed circuit boards, automation, robotics, Python

This would include researching and putting together a rig for testing voltage regulator noise suppression, testing a number of commercially available voltage regulators (they need to be put on simple evaluation PCBs), testing them and comparing the results. Time allowing, we'll design a power supply based on the best contender.

Working in the LLTL on the cryogen-free system alongside PhD students and postdocs.

Prerequisite: Hands on lab skills, python

Location: On campus only

The T2K experiment measures neutrino interactions from the J-PARC neutrino beam in Japan. The experiment measures the neutrinos in two detectors, one close to the beam production point and one 300 km away, after neutrinos undergo oscillation. T2K has collected one of the world’s largest neutrino datasets, and we are interested in understanding how well the data matches our models of how the neutrinos are produced, oscillate, and interact.  The project will focus on developing different techniques for assessing how well T2K data matches the predictions from theory, starting by assessing the techniques simple models and then moving to using the large data set from the T2K near detector. 

Prerequisite: Python programming

Location: On-campus or Hybrid

This is a project in theoretical physics.  The mechanism of high-temperature superconductivity in doped Mott insulators, as realized in cuprates, remains a central problem in condensed matter physics. This project will focus on canonical models of strongly correlated electron systems, such as the single-band Hubbard model and the three-band Emery model, to study the interplay between superconducting correlations and Mott physics. Building upon previous work in my group [1,2], this project will explore the dependence of several superconducting properties on the bandstructure parameters. 

[1] Walsh et al., PRB 108, 075163 (2023); [2] Fratino et al., PRB 93, 245147 (2016)

Prerequisite: PH3710: metals and semiconductors; this project requires a good understanding of quantum theory of solids, and  enthusiasm for mathematics and computation.

Location: On-campus

Below are a list of sub-projects that maybe included in placement. Some sub-projects can be completed in a week, while others may take longer. This is a flexible role where you will be steered towards appropriate sub-projects that can all be completed within the placement timeframe.

• Upgrade the computer in the dome (new OS, new data acquisition software)
• Produce/update documentation for observatory use
• Take solar data for the limb darkening project (for use in winter, when the Sun is too low)
• Improve the telescope's tracking and polar alignment
• Integrate the smaller scope with the LX200 as an auto-guider
• Improve the floor insulation and helping with efforts to mitigate the hot-air flow sources on the roof
• Restore the desiccant in the CCD camera
• Assist the Telescope Committee in plans for the robotic telescope
• Assist Ian and GDC in cleaning the Schmidt corrector plate

Location: On-campus

Kilometre-scale Michelson interferometers such as LIGO are now routinely used to observe the violent collisions of black holes and neutron stars. Unlikely traditional telescopes, these do not work by “pointing” at the event, but “listening”. Therefore, to localise an event, a network of detectors is needed: the time-delay between detectors then enables us to infer where the event occurred on the sky. This is important as after a pair of neutron stars collide, they produce a kilonova electromagnetic counterpart. To find this counterpart, observatories scan the sky: better localisation from the detectors can therefore reduce the sky area they need to cover and increase the chance of finding the counterpart. In this project, you will build a toy model showing how a network of detectors can localise a signal. You will use a Raspberry pi and a water-level module to find disturbances in a pond.

Prerequisite: Python programming

Availability: run during early summer only

The Vela pulsar is a young energetic radio pulsar that is continuously observed by the Mount Pleasant observatory in Hobart, Tasmania. In 2016, the team made the first direct observation of a so-called “glitch” in the pulsar. This is an event where the pulsar suddenly increases its rotation rate by about 1 part in a million. Many glitches have previously been inferred, but no one has seen a pulsar undergo a glitch before. Their unique data set revealed that just before the glitch, the pulsar switched off: an event known as a null. In this project, you will analyse the raw data from the Mount Pleasant observatory, building an algorithm to detect nulls and quantify the significance of the null on a large span of 100’s of days of data.

Prerequisite: Python programming

Availability: run during early summer only

Usually in a low temperature measurement of a metal the electrons and the lattice are at the same temperature. However at liquid helium temperatures, T= 4.2 K, the electrons can be heated using a current, such that they will come into local equilibrium and can be described by a Fermi function with a temperature greater than that of the lattice. We are interested in how the hot electrons lose their energy via conduction through the leads and by emitting acoustic phonons.

In this project we will use two electron thermometers:  Johnson noise and thermopower. The samples will be 100 angstrom thick two-dimensional electron gases, that have been processed in the clean-room at Royal Holloway. The measurements link up with a wider project (with UCL, Cambridge and Birmingham) looking at how to decouple 2D electrons from their environment to observe new quantum physics.

You will work with us, myself + post-doc in the lab. Between us we have a variety of skills: measurement, electronics, sample design, cryogenics, semiconductor physics, quantum mechanics, cleanroom processing, Python, etc. 

Notes: This is an experimental project. A willingness to fiddle and learn in the lab, plus keeping a lab book, are all essential skills. 

The global target to reduce carbon emissions has brought research into thermoelectric materials centre stage, since they offer the potential to convert waste heat into useful electrical power. Zinc oxide is attractive for energy harvesting because of its simplicity, high thermal stability, corrosion resistance, non-toxicity and low cost. In bulk form, its electronic transport is ideal for thermoelectric applications, but its thermal conductivity is too high. Nanostructuring is found to dramatically improve its thermal conductivity and thermoelectric performance. We have measured the phonon excitations responsible for the transport of heat in nanostructured zinc oxide using inelastic neutron scattering at the ISIS Facility. The aim of this project is to analyse neutron scattering data by computer modelling, to understand the thermoelectric behaviour. 

Prerequisite: Python programming

Location: On-campus

Thin films are essential components in quantum technologies, and understanding their structures is key to optimising device performance. Using the new Bruker D8 Discover X-Ray Diffractometer at Royal Holloway we are able to determine the structures, thicknesses and interfacial roughness of the thin films, as well as their epitaxial relationship with the substrate. In this project you will perform a variety of x-ray experiments including high-resolution x-ray diffraction, x-ray reflectivity and grazing-incidence x-ray diffraction on a thin-film sample used in the fabrication of superconducting qubits, and you will model your data with the instrumental data analysis software.

Prerequisite: None

Location: On campus

London Low Temperature Laboratory at Royal Holloway is a leading centre of experimental research on quantum materials down to ultra-low temperatures of order 0.0001K. These studies employ a large array of instruments, including digital oscilloscopes, function generators, lock-in amplifiers, frequency counters, etc. This project is devoted to developing Python software for controlling this array of devices, taking and recording the data,  with convenient graphical user interface and remote control capabilities. The project will help the student develop versatile IT skills and may involve participation in ultra-low-temperature experiments.

John Adams Institute for accelerator science has successfully acquired sub-THz equipment to be used for long wavelength non-destructive tomography of biological objects.

The project will aim in commissioning of new emission, transportation and detection equipment, measure its power, spatial and spectral characteristics. 

Imaging techniques for tomography will be tested and documented.

This project aims to take raw data from neutron scattering experiments on metallic magnets near quantum critical points and analyse them with modern software packages and/or Python coding to allow an interpretation of the data in terms of magnetic structure models and models of low-energy magnetic excitations.

Prerequisite: familiarity with solid state physics and with Python

Location: Hybrid

Dark matter is thought to make up over 25% of our Universe, yet what it is remains a complete mystery. Many experiments have been built to determine what dark matter is. These have typically focused on supersymmetry-inspired dark matter candidates, with GeV to TeV scale masses, but after many years of searching, no evidence has been found. This has resulted in new ideas for experiments that look for relatively unexplored regimes of dark matter, particularly small mass dark matter, such as superfluid experiments that will search for MeV particles, which RHUL is heavily involved with.

This project will consider the opposite regime of dark matter – very heavy particles (around 0.01mg) that have recently been predicted to be a consequence of quantum gravity: For example, in loop quantum gravity, tiny primordial black hole remnants formed in the early Universe are thought to be promising candidates for dark matter. Seeing such dark matter would, therefore, also provide experimental evidence of quantum gravity.

The project will involve applying recent ideas on how to test such heavy dark matter to superfluids, which, due to their large density and quantum properties, appear promising systems for such tests. The end goal is to estimate what levels of improvement in superfluid technology, if any, would be required.

Prerequisite: A basic knowledge of quantum mechanics is required, but nothing high-level: e.g. knowledge of the time-dependent Schrödinger equation and how to calculate expectation values. A basic knowledge of dark matter would be useful but not essential. Some experience with quantum optics, e.g. annihilation and creation operators and quantum states, would also be useful but not essential. The project is a theoretical physics project.

Location: On-campus, hybrid, and remote options available

Coding project, optimising searches for new physics for Run 3 in dilepton data.

Prerequisite:  Programming aptitude

Location: Hybrid and remote options available

To analyse data related and work on projects related to equity, inclusion and diversity.  

Prerequisite:  Analytical skills

Location: Hybrid and remote options available

The JAI group for particle accelerator research at RHUL is developing technology to convert 3D accelerator models into formats for use in augmented & virtual reality (AR/VR) environments. To explore new directions for this technology, we are aiming to test 3D cameras to capture images of the physics department to develop into outreach material. In this project, the student will explore 3D capture technology by imaging areas of the department. This will focus primarily on image capture, development of interactive models embedded in websites, and exploration of converting said material into a VR environment for display at outreach & open days.

Notes: Experience with 3D modelling & VR development in Unity/Unreal/Blender is highly desirable.

* This project is available subject to the appropriate hardware and associated funding being sourced

This project proposes to analyse images and videos from different sources in a lab setting. Our main goal is to understand these images and videos coming from traditional dial gauges using a camera. We will use a handy tool called OpenCV in Python to help us do this. Our student will start by learning how OpenCV works and then create a simple code that can understand what is happening in the images and videos. Once we have this code working well, we'll connect it to things like webcams or devices like Arduino, which we will put near the relevant gauges in the lab. This way, we can get real-time data from these gauges. Later, we will try to use the same code for digital setups, making it even more useful. So, if you like working with Python, can manage tasks on your own, and want to learn more, this project is a great opportunity.

Prerequisite: We're looking for a student who's curious, enjoys working with Python, and wants to learn about pictures and videos in labs. You should be able to work independently and be excited to improve your skills.

Location: On-campus and hybrid options available