Name

Department

Project Title

Project Description

Guidance

Dr Resul Umit

Government & Int Affairs

Building a Comprehensive Candidate-Level Election Dataset for the UK (1832–2024)

This project will create the most comprehensive dataset of candidate-level election results in the UK, covering the elections between 1832 and 2024. By gathering and verifying records from various archival sources, it will significantly enhance data reliability and make information more accessible in digital form. Once finalised, the dataset will be publicly available, encouraging wide use both within and beyond academia.

Prof Darren R. Gröcke

Earth Sciences/ Archaeology

Characterising health and nutrient variability in agricultural soils

In 1937, US President, Franklin D. Roosevelt warned that “the nation that destroys its soils destroys itself.” Traditionally, UK farming has focused on "yield per hectare," but the future of agriculture demands a shift towards "health per hectare." Achieving a healthy and productive farm requires a comprehensive understanding of soil nutrient balance: a sustainable balance ensures a bountiful harvest, while under- or over-feeding the soil leads to failure. Maintaining soil health is fundamental to the future of food security in England. To continue farming in the current manner will lead to the destruction of our agricultural soils. Nutrient variability in the soil is critical to productivity and environmental conservation. In this feasibility study, we will build on a pre-existing collaboration between academics and the Lynster Farmers Group (LFG), to employ multiple field-based experiments in the Lyth–Winster catchments, Cumbria, NW England. The project will generate detailed surface and subsurface nutrient (e.g., N, P, K) maps across diverse farmland types (e.g., arable, pastoral, dairy). Nutrient landscape mapping will accurately advise present and future nutrient application rates, and therefore, more effective soil management. The student will: undertake a field survey in Cumbria on multiple farms; collect multiple 50cm-deep cores geospatially in the field; soil cores will be analysed for pH, carbon, nitrogen, phosphorus content and microbiome community.

Background in environmental science and bioscience is desirable.   The project duration is only for 8 weeks in July and August.

Kyle Oman

Physics

Dark matter & radio astronomy in the SWAN Universe

Identifying the particles making up the dark matter whose presence we infer throughout the Universe is a key objective in physics and astronomy. Different hypotheses for the nature of dark matter lead to different predictions for the number of lumps of dark matter that provide the formation sites for galaxies, especially at the low-mass end. The Matariki Network-funded "SWAN Universe" project is a global collaboration of experts in observational and theoretical astrophysics (including Prof. Kristine Spekkens at Queen's). We are testing the predictions of cutting-edge computer simulations of galaxy formation assuming different dark matter particle candidates against observations from the WALLABY 21-cm radio astronomical survey. WALLABY is now in full swing on the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, and we have just completed the first simulations for the project; you will join the project at this exciting time. You will use a virtual radio telescope to 'observe' galaxies in our simulations, faithfully mimicking ASKAP observations. You will then run the WALLABY kinematic analysis pipeline on the simulated observations to measure the dark matter content of the galaxies. Comparing against the known dark matter content from the simulations will allow you to check for biases that could hinder our ability to count low-mass dark matter clumps and test dark matter hypotheses. You will be hosted in Dr Kyle Oman's group (https://kyleaoman.github.io) at Durham University's Institute for Computational Cosmology.

The project can be of 8, 10 or 12 weeks duration, to be agreed with the successful candidate, with an end date no later than Aug 8. Preference will be given to applicants with a foundational knowledge of astronomy and strong computer programming skills, please address this in your application statement.

Dr Vanessa Ward

Chemistry

Designing next-generation batteries: tracking and predicting the movement of lithium ions

A sustainable future on our planet relies on efficient energy storage. As we source more of our energy from renewable power plants, we are increasingly relying on an intermittent supply of energy. Batteries with large storage capabilities are required to use these energy supplies efficiently. The current generation of lithium batteries are not suitable for this task. Not only do we need to increase efficiency and energy densities, lithium batteries require safety improvements to prevent frequent fires and explosions.  Solid-state batteries, which are safer and capable of storing energy at greater densities, have been highlighted as a next-generation solution. In this project you will study materials that are potential solid electrolytes. Given the central role that solid electrolytes possess in solid-state batteries, understanding novel solid electrolyte materials is vital for the future of this field.   For batteries, conductivity and hence the movement of ions through a material is critical. In this project you will use computational methods to analyse lithium ion diffusion. You will also work closely with experimentalists in the group of Dr Karen Johnston, using solid-state NMR to study new materials for next-generation batteries.   You will use Molecular Dynamics simulations as well as bespoke analysis techniques to track lithium ion movement. You will use these techniques to bridge time- and length-scales from the atomic level up to macroscale properties, uncovering new phenomena. As well as understanding materials that are currently studied as potential electrolytes, this work will help to predict and design materials that will define our future.

Physics/Chemistry/Maths or a related subject. Some experience in computer programming (in any language) is required. Any project duration will be considered.

Prof Darren R. Gröcke

Earth Sciences/ Archaeology

Determining the presence of pathogens in north-east coastal peracarid crustaceans

Pollution derived from human activities have dramatically changed the coastal environment and increases the potential for these environments to harbour pathogens. Pathogens can enter the marine environment through sewage and faecal waste, which has become significantly more prominent in English rivers and estuarine-coastal environments. Sewage and faecal waste contain a cocktail of bacteria, parasite cysts, and viruses, which, when entering the marine environment, become incorporated into marine ecosystems. Most human illnesses associated with exposure to marine waters are due to viral infections, as recorded in the UK over the past year. Given the volume of waste introduced into marine waters, even a small number of pathogens or their toxins (biochemicals) suggests a non-trivial risk for exposure and impact. At present we do not fully understand the direct potential for disease associated with human waste in marine systems. This pilot project will assess pathogen uptake and the characterisation of said pathogens in marine and estuarine peracarid crustaceans (Amphipoda) - a group of arthropods, typically less than 2cm in length, that are detritivores and/or scavengers. Peracarids have been chosen for this pilot study due to their ubiquity in marine environments, feeding niche, and importance to the basal foodweb. The student will: collect peracarid crustaceans at select points along the North Yorkshire coast (i.e., natural versus sewage outflows); they will be screened for disease using histopathology; and then stable isotope analysis will be performed to determine if the ecological niche of diseased versus non-diseased amphipods is impacted.

Background in environmental science and bioscience is desirable.  The project duration is only for 8 weeks in July and August.

Dr Bronwyn Reichardt Chu

Physics

From Blurry to Brilliant: Resolving the Role of Stellar Feedback in Galaxies

Have you ever wondered why galaxies don't turn all their gas into stars at once? Or how galaxies in the past are related to those we see today? The answer lies in the process of "stellar feedback" - the way that massive stars interact with the gas surrounding them.  Stellar feedback regulates how efficiently new stars can form, and determines whether gas remains within the galaxy or is pushed out.  Characterising stellar feedback - its strength, its impact on the formation of more stars, and its role in determining the fate of gas in galaxies - is key to fully understanding galaxy evolution. In this project, you will use Keck telescope optical data from the DUVET survey to study gas expelled from galaxies (known as star formation-driven outflows), and link these outflows to the on-going star formation within the galaxy's disk.  You will examine how spatial resolution influences our ability to detect and characterise these outflows.  By comparing our results from nearby galaxies to those from high-redshift (early Universe) galaxies, you'll investigate how stellar feedback has changed over cosmic time.  This project will give you hands-on experience in data analysis and Python programming. You'll develop your skills in handling real observational data, analyse IFU cubes, and contribute to our understanding of the life cycle of galaxies.  If you're curious about galaxies and want to dive into observational astrophysics, this project is for you!

Interest in observational astronomy, some familiarity with Python would be preferred, but not essential

Martin R. Smith

Earth Sciences

Quantifying the limits of morphological evolution

Despite increasingly sophisticated models of molecular evolution, the underlying principles that guide the evolution of anatomical characters remain obscure. Our group has developed models of morphological evolution that account for possible, but unobserved, evolutionary outcomes.  This project will use existing datasets of living and fossil animal groups to explore the factors that constrain observed patterns of evolutionary change.  Our models allow us to quantify what proportion of evolutionary possibilities have been explored through a group's geological history.  We will use this information to explore whether modes of evolution differ between different animal groups (e.g. vertebrates vs invertebrates, marine vs terrestrial), and through different points in geological time (e.g. when a clade is young or old, or undergoes an extinction event).  The project will involve: 1. Annotating datasets of discrete morphological characters. 2. Analysing datasets using our existing workflows. 3. Compiling results to identify patterns through time and between animal groups  4. Synthesizing results into an evolutionary framework.

A student would benefit from familiarity with evolutionary biology, statistical analysis, or computational scripting, but these are not necessary as relevant training will be provided.

Professor Alton Horsfall

Engineering

Quantum beyond the laboratory

The formation of atomic point defects in wide bandgap semiconductors, such as diamond and silicon carbide has enabled new opportunities in quantum technology for the realisation of novel high performance sensors, timing systems and hybrid systems.  Optimisation of the type of defect in silicon carbide is enabling the enhancement in sensitivity of magnetometers that offer SQUID level performance without the need for cryogenic cooling and complex shielding.  Recent work in the department of engineering has demonstrated a new technique for the interrogation of these quantum defects in silicon carbide devices that offers an enhanced signal to noise ratio. Crucially, the technique has the potential to be expanded to give a vectorised magnetic field measurement, linked to the differences in crystal structure, giving a world leading capability in the mapping of magnetic anomalies.  Working as part of the research team and in collaboration with a local semiconductor company, the project is to design a portable version of the magnetometer that can be used to map the magnetic fields generated by current carrying cables that are buried underground.  The development will involve quantum physics, a variety of engineering challenges and some computer coding to support the final integration in to a ruggedised system that can be easily transported around the city.   As a member of this project, you will gain hands-on experience in data collection, validation, management, and coding. Previous familiarity with Excel, R, or Python is helpful, but not required, as training will be provided to develop essential quantitative skills. You will also be encouraged to pursue your own research question using the final dataset, which could form part of assessed coursework, a dissertation, or be published as a blogpost. If you decide to pursue this option, you will be supported throughout the project with structured guidance and regular feedback.   This opportunity will particularly interest those studying politics, data analytics, or history, although students from computer science, economics, or statistics who wish to apply their knowledge to political data are equally welcome. Overall, the project offers undergraduates or postgraduates the chance to combine historical exploration with modern computational methods, providing a strong foundation for both academic and professional development.

No formal pre-requisites, but a working knowledge of physics or electronic engineering is highly desirable and a willingness to learn new ideas and implement them in imaginative ways essential

Martin R. Smith

Earth Sciences

Using Bayesian approaches to correlate geological data

Reconstructing the history of the planet, its life and environments requires accurate correlation of the rock record between different depositional environments and geographical locations. Such work often relies on manual examination of proxy data such as the isotopic composition of sediments. This leads to subjective correlations with potentially large uncertainty.  Our working group has developed a model that combines geochemical data and absolute age estimates from radiometric dates in the Bayesian framework. The program is written in R, and works by evaluating the fit of Bayesian splines to different alignments of the geochemical data, using Markov chain Monte Carlo (MCMC) methods to obtain the posterior distributions.   This project will apply our model to geological datasets chosen by the student, in order to produce objective correlations with quantified uncertainty. The results and their implications will be evaluated and their significance for a geological problem explored.

This project is suitable for a student with a mathematical, statistical or geological background.