October 2026 Entry: Available Research Projects
Prof. James Kermode
HetSys Director
Exciting New Projects to Explore
We are now recruiting for a selection of fully funded PhD projects across the diverse HetSys research areas. These projects span a wide range of cutting-edge topics, offering you the chance to work on impactful challenges at the interface of science, engineering, and computation.
Our funding package covers tuition fees, provides a tax-free stipend at the UKRI rate to support living expenses, and includes a research training support budget. Our projects are open to UK, European and Internatonal applicants.
Alongside your research, you will benefit from the HetSys training programme, designed to develop high-quality computational scientists who:
- Thrive in interdisciplinary environments
- Communicate effectively across disciplines
- Are well-prepared for a broad spectrum of future careers in areas where there is clear and growing demand
Explore the full list of projects below and start your journey today—join a vibrant community tackling real-world challenges!
Each project has been tagged with keywords that show the HetSys length scale and themes it relates to:
Quantum – electrons, atoms and molecules for catalysis, medicines and devices
Atomistic – materials structure, phases and defects for properties and applications
Continuum – new methods for fluids, plasma, porous media and composites for technological solutions
Materials characterisation – linking cutting-edge theory with experiment to characterise and design materials
Modelling of biological and biomedical systems – multiscale and multiphysics modelling for pharmaceuticals and health
Mathematical and statistical methods & algorithms – including novel UQ, SciML and quantum algorithms
Dr Emmanouil Kakouris (Engineering) Project Partner: AWE-NST |
When materials are pushed to their limits, such as during high-speed impacts or other extreme loading events, they can fail in ways that are still not fully understood. This project offers the chance to uncover the science behind these processes and help design the next generation of safer, stronger materials. You will combine advanced modelling techniques with scientific machine learning to create intelligent simulation tools that accelerate and enhance conventional methods, enabling faster and more accurate predictions of how metals behave under extreme conditions. Your work will directly address real engineering challenges and advance materials innovation and safety. |
Continuum |
Prof. Gabriele Sosso (Chemistry) Project Partner: AWE-NST |
Accurately predicting how gases move through organic materials such as polymers underpins major challenges - from reducing hydrogen crossover in fuel cells to controlling gas transport that drives battery degradation. The key challenge is to build models that capture gas/polymer interactions and ageing with quantum-level accuracy at the larger scales of real materials. This project will train machine-learning models on high-quality quantum data, use them for molecular simulations, and connect the results to continuum models via reproducible multiscale approaches. The focus will be on gas/polymer systems relevant to AWE-NST, a UK stakeholder promoting fundamental science with practical impact. |
Atomistic |
Dr Bora Karasulu (Chemistry) Project Partner: Merck Electronics |
This project, co-funded by Merck Electronics, will use computational simulations to study how thin films form during flowable chemical vapor deposition (FCVD), a process used to build advanced semiconductor devices. Unlike traditional CVD, FCVD creates a liquid-like layer that fills narrow trenches on substrates before solidifying into films. We will model this across multiple scales, from atoms to fluid flow, using quantum chemistry, machine learning, molecular dynamics, and fluid mechanics. We aim to understand how chemical structure of precursors and process conditions affect film quality, helping design better materials and manufacturing methods in close collaboration with the Merck experimental team. |
Atomistic |
Dr Livia Pártay (Chemistry) Project Partner: AWE-NST |
Join a PhD project that goes beyond state-of-the-art to explore the phase behaviour of potassium and unlock new understanding of alkali metals' unique physical properties. At high pressures and temperatures, these metals reveal complex phase transitions and exotic crystalline structures that remain poorly understood despite extensive experimental investigations. This project combines cutting-edge sampling techniques with developing machine-learned potentials for accurate phase predictions, offering opportunities for method development with broad, long-term impact. Not only will you gain insights into fundamental atomistic properties of alkali metals, but you'll also contribute to pioneering computational tools that extend far beyond potassium. |
Atomistic |
Dr Ferran Brosa Planella (Engineering) Project Partner: Jaguar Land Rover |
Lithium-ion batteries are essential for electric vehicles and achieving net-zero targets, but they degrade over time, reducing performance and safety. A key issue is gas generation, which causes internal pressure build-up and can lead to cell failure, but existing models struggle to capture this complex, multiscale phenomenon efficiently. This project will develop a novel, physics-informed surrogate model using Bayesian machine learning to predict gas generation and pressure evolution. Our approach will combine physical insight with data-driven techniques and uncertainty quantification, offering fast, reliable predictions and contributing to safer, longer-lasting batteries. |
Continuum |
Dr Thomas Hudson (Maths) Project Partner: Syngenta |
Many industrial companies dry their chemical products into powders to aid wide transportation and distribution. This process often takes place in large scale driers where the material is heated and broken up mechanically with mixing blades. However, under certain conditions the process may break down: the material sticks to the edges of the drier, causing wastage of the product, and in extreme cases, deterioration of the drier itself. This research aims to develop a new multi-physics model to describe the material properties of these drying powders, with the aim of identifying the culprits of wall adhesion. The project is suitable for an applied mathematician, or physicist with a strong mathematical background. |
Continuum |
Dr Michael Faulkner (Engineering) |
Glasses are materials that combine macroscopic solid behaviour with amorphous liquid-like structure. These mysterious signature properties are ubiquitous across science and engineering, with examples ranging from optical fibres to novel formulations of pharmaceutical drugs and beyond. Understanding these materials experimentally is, however, a real challenge due to very long relaxation timescales that preclude many experimental measurements. Modelling is therefore paramount, but traditional simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state-of-the-art simulation algorithms to circumvent the slow dynamics leading to high-quality modelling of currently inaccessible experimental quantities. |
Atomistic |
Dr Radu Cimpeanu (Maths) |
We will develop new mathematical models and control methods for falling liquid films. Why falling liquid films? They form a beautiful theoretical setting with applications in microchip manufacturing and smart printing. How will we do it? Using precise jets that blow air into the fluid from above, improving on recent work on control that injects or removes fluid from below these films, which is less practical. This project will involve model development and validation, numerical simulation, and control development under model uncertainty and noisy observations of the system. It offers very interesting development directions which we look forward to exploring together. |
Continuum |
Prof. Scott Habershon (Chemistry) |
Protein folding is a dynamic, complex process by which proteins traverse the free energy landscape, thus connecting the unfolded and the folded (native) state. Understanding protein folding mechanisms is a key route to better understanding misfolding-related diseases such as Alzheimer's - but predicting how proteins fold in biological environments remains a key unmet challenge. This project brings together insights from efficient graph-driven folding simulations with mass spectrometry experimental data, creating a unique multi-stranded methodology to map out free energy landscapes associated with protein folding in environments spanning gas-phase to microsolvation environment. |
Atomistic |
Dr Chris Patrick (Physics) |
In the development of sustainable materials and manufacturing, residual stress forces hidden within a material can cause serious problems, compromising performance and reliability. Magnetic non-destructive testing (mNDT) offers a highly efficient route to detect such stresses, but a rigorous theoretical framework to interpret measurements is still lacking. This project addresses this gap by combining quantum mechanical calculations with continuum micromagnetic theory to bridge atomic and macroscopic length scales. Developing a predictive, fundamental theory linking stress to measurable magnetic signals will widen the applicability of mNDT and support the design of next-generation materials. |
Materials characterisation |
Dr Tobias Grafke (Maths) Project Partner: ECMWF Machine Learning Group |
Generative machine learning techniques, as known for example from large language models or image generation, have recently also taken the atmospheric dynamics and weather forecast communities by storm. At the same time, due to antropogenetic climate change, society faces elevated risk from extreme weather events such as flash floods, heatwaves, or tropical cyclones. This project aims to combine generative weather prediction models with rare event techniques to generate plausible trajectories of the weather system exhibiting extreme weather events. The ECMWF machine learning division has agreed to provide data and support the project in adapting their in-house intermediate-resolution generative weather model. |
Continuum |
Dr Livia Bartók-Pártay (Chemistry) |
Organic matter encompasses a range of length scales from small molecular units to long chained polymers, with even simple substances such as nitrogen or methane exhibiting complex phase behaviour. Methods to map this behavior are well established, but are sufficiently laborious that making predictions for all but the simplest organic molecules is intractable. This project focuses on nested sampling (NS), a novel 'one shot' sampling method which can reveal previously unknown phase equilibria. In this project we will extend NS to incorporate collective Monte Carlo moves designed for flexible molecules, extending the applicability of the technique to complex organic substances. |
Atomistic |
Prof. James Kermode (Engineering) Project Partner: Max Planck Institute for Sustainable Materials |
Develop cutting-edge machine learning models to predict how materials break at the atomic scale. You'll create AI-driven simulations that reveal why tungsten, the leading fusion reactor material, transitions from ductile to brittle behaviour as the temperature drops, combining quantum mechanics, large-scale molecular dynamics, and deep learning. Work with world-leading researchers at Warwick and the Max Planck Institute for Sustainable Materials, mastering scientific machine learning, uncertainty quantification, and high-performance computing. Your models will inform fusion design and advance AI-for-materials. Perfect for physics, maths, or materials students wanting to blend fundamentals with real-world impact. Code, physics, and helping to solve the energy challenge, all in one project. |
Atomistic |
Prof. James Sprittles (Maths) Project Partner: UK Met Office |
How do countless microscopic droplets shape the weather we see? This project tackles that challenge - linking the physics of droplet collisions in turbulent clouds to the rainfall and climate patterns predicted by forecasters. You'll use high-fidelity simulations to uncover how droplets merge and grow, and work with partners such as the Met Office to embed this understanding into their weather and climate models. Along the way, you'll develop new mathematical and computational models, and test ideas against real-world data - perfect for students who enjoy applied maths, computation, and making 'models of matter that matter'. |
Continuum |
Dr Albert Bartók-Pártay (Engineering, Physics) Project Partner: University of Birmingham |
Molecular optoelectronic devices have a high potential in ultrasensitive detection, nanoscale electronics, and medical imaging, but finding promising molecules for these applications is challenging because exceptional optical and electronic performance needs to be balanced with stability and ease of synthesis. Current AI tools can explore vast chemical spaces but often suggest molecules that are unstable or hard to make. This project develops a generative AI framework that utilizes machine learning predictions and quantum chemistry simulations to design stable, synthesizable, high-performance molecules. The framework will integrate multi-objective optimisation and multi-fidelity active learning, creating a practical route to discovering new molecules for optoelectronic devices. |
Quantum |
Prof. Mark Senn (Chemistry) |
Ferroelectric perovskites are promising materials for device applications due to rich topological aspects to their polarisation domain structure. This mesoscale structure can be controlled with strain engineering. The relationship between crystal structure, strain and local polarisation can be understood at the length scale of a few repeat units of the crystal structure. However, to predict and ultimately design domain structure we need effective models on much larger length scales. This project will develop exactly this capability, building minimal on-lattice models to capture the essential physics and parameterise/validate these via analysis of experimentally obtained images. |
Quantum |
Prof. Neophytos Neophytou (Engineering) Project Partner: University of St Andrews |
Advancements in materials synthesis have allowed the realization of many novel materials and their alloys, which are gradually finding their ways into numerous applications including energy, sustainability, medicine, novel computation, etc. A major direction of interest is their electronic properties. However, the accurate assessment and prediction of electronic transport is a highly challenging task. The project uses Machine Learning (ML), in combination with DFT and state-of-the-art Boltzmann transport methods, to predict, accelerate, and scale the computation of electronic properties of complex materials and their alloys. The richness of experimental data from literature and project partners will aid towards model validation. |
Atomistic |
Dr Sara Sangtarash (Engineering) Project Partner: University of Liverpool |
Thermoelectric materials convert heat into electrical energy, crucial for sustainable power and waste heat recovery. Their efficiency is measured by the figure of merit, ZT. Achieving high ZT requires a delicate balance: high electrical conductance (G) and Seebeck coefficient (S) with low thermal conductance (k). Graphene Nanoribbons (GNRs) are promising but currently, designing high-ZT GNRs is a slow, trial-and-error process, as the inverse problem is computationally intractable. This project uses an AI-guided inverse design loop. A goal-directed AI proposes novel GNR architectures, which a fast machine learning model rapidly evaluates, accelerating the discovery of next-generation thermoelectrics. |
Quantum |
Dr Thomas Hudson (Maths) Project Partner: University of North Carolina, Charlotte USA |
When applied to molecular dynamics simulations of large numbers of atoms, coarse-graining approaches seek to reduce of the number of degrees of freedom in a material model, thereby reducing computational costs and the environmental impact while maintaining accuracy. Although such approaches are widely used, there is still much theoretical development needed to assess the accuracy and efficiency of these approaches. This project therefore seeks to study the mathematical theory behind coarse-graining approaches, providing new understanding of the size and sources of errors committed when they are used. The project will involve both development of mathematical theory in concert with careful numerical experiments. |
Atomistic |
Dr Peng Wang (Physics) |
Magnetic skyrmions are tiny whirlpools of spins that could form the basis of future low-power data storage devices. However, real skyrmions are three-dimensional and can twist, stretch, or deform when trapped by material defects - behaviour that is still poorly understood. This project will develop advanced computational models to simulate a new imaging technique called electron ptychography, which can map magnetic fields in 3D at nanometre resolution. By combining quantum-mechanical modelling, tomographic reconstruction, and data-science methods, the project will reveal how skyrmions interact with defects, helping to design the next generation of magnetic materials. |
Quantum |
Dr Peter Brommer (Engineering) Project Partner: Speciality Steel UK & British Steel |
Steel recycling is a key strategy to reduce the carbon emissions related to steel production. However, producing advanced steel grades from scrap leads to a higher level of impurities which may be detrimental to the steel properties. Elements such as copper and tin, which are the focus of this project, enrich at grain boundaries during thermo-mechanical processes used to achieve the desired steel microstructure. In this project, you will establish a digital route to quantify the segregation behaviour of residual elements at austenite/austenite grain boundaries through atomic-scale simulations, using modern machine learning techniques and in close interaction with experimental work. |
Quantum |
Dr Peter Brommer (Engineering) Project Partner: French-German Research Institute of Saint-Louis |
Two-dimensional materials, such as graphene, could be used in molecular sensors - if we can control and tune their properties. You will develop and use top-of-the-line machine learning models to predict the sensor response of these materials under realistic conditions, including in liquids. Combining quantum mechanics and atomic simulation with AI-driven sampling techniques, you will determine terahertz and Raman spectrograms to directly compare to measurements obtained in the THz labs at Warwick and by our collaborators at the Institute of Saint-Louis (ISL). By suggesting design modifications to the molecular structures, your work will improve the next generation of molecular sensors. |
Quantum |
Dr Ferran Brosa Planella (Maths) |
Heating accounts for nearly half of global energy use and 40% of energy-related CO² emissions, making it a key target for decarbonisation. A promising option to store energy and support a low-carbon grid is thermochemical energy storage (TCES), where heat is stored/released through reversible chemical reactions. This project focuses on NaOH water TCES systems, which use cheap, abundant materials. We will develop modelling tools that combine physics-based and data-driven methods to support the design and scale-up of these systems. This approach will reduce the need for costly experiments, improve scale-up predictions, and provide confidence intervals to support better design decisions. |
Continuum |