New PhD Project Opportunities 2024
Project Spotlight
We're putting the spotlight on some of our PhD projects, currently recruiting for a 2024 start.
Applications welcome from UK students,
click here.
The University of Warwick has been awarded £11m to train PhD students in computational modelling.
HetSys recruits enthusiastic students from across the physical sciences who enjoy using their mathematical skills and thinking flexibly to solve complex problems to join our training programme.
For guidance on how to apply, student funding, the integrated HetSys training programme and what life is like in the HetSys CDT, please visit the Study with Us page.
The following projects are now recruiting for a September 2024 start:
Note: We are still accepting applications from UK students. The application window for overseas students has now closed.
| Deep learning of reaction barriers for high-throughput retrosynthetic drug design |
The drug discovery pipeline involves the screening of many molecules before viable leads are identified. This involves screening for their pharmacological properties, but also for their synthetic viability. Typical drug molecules can contain up to 100 non-hydrogen atoms, which makes the development of cost-effective and efficient synthetic pathways very challenging. Therefore, high-throughput screening of drug-like molecules needs to also consider their synthetic viability. The aim of this project is to develop a deep learning and generative design toolchain to accurately predict chemical reaction barriers that will advance chemical retrosynthetic design workflows. The project is in collaboration with a leading pharmaceutical company and will involve an extended industrial placement |
Quantum |
| Better-conditioned Inverse Problems in Computational Materials Science |
Inverse problems are a general class of problems that involve calibrating the parameters of a model using measurements of its outputs, typically from real-world experiments. Many such problems occur across computational science, e.g. in the calibration of constitutive parameters such as elastic moduli (and other examples below) on the basis of computational simulations. However, these problems are often mathematically ill-posed, meaning there is no single, stable, well-defined solution. This issue may be resolved numerically either using classical optimisation approaches which select a single solution (that may be an artefact of the choice of optimizer) or using tools from statistics and machine learning such as Bayesian inference which mitigate the ill-conditioning of the problem by incorporating prior information. |
Atomistic |
| Building better batteries: modelling and optimisation of electrode filling |
Manufacturing not only has a significant impact on battery performance and lifetime, but also on cost and environmental impact. A key process (yet not a well-studied one) is the so-called filling, in which a liquid electrolyte is incorporated into the battery, occupying the pores in the electrodes. It requires keeping the battery at high temperatures for days, becoming a very expensive process both in terms of time and energy usage. In this project, you will have the opportunity to build exciting new capabilities for modelling and optimisation of electrode filling, with a potential to energise our understanding of battery manufacturing. |
Continuum |
| Hybrid modelling approaches for moving fluid-fluid interfaces around solid obstacles |
Interfacial fluid flows around obstacles and through porous materials are key to numerous applications, including filtration, decontamination and manufacturing. For instance, resin must be injected into a porous mesh, without trapping air bubbles, to manufacture composite materials. Interfacial flows are difficult to model and simulate accurately, and in porous media the multiple disparate lengthscales further complicates matters. However, this multi-scale setting also provides beautiful mathematical modelling opportunities. In this project we will develop and use hybrid modelling approaches for moving fluid-fluid interfaces around obstacles, incorporating analytical and computational techniques, to investigate questions such as minimising defects in composite manufacturing. |
Continuum |
| Something in the air: predicting the behaviour of nanoparticle aerosols |
The World Health Organisation recently classified air pollution as “the single biggest environmental threat to human health”. The airborne particulate matter thought to be most harmful is at the nanoscale – particles so small that they can evade our respiratory defence systems. Evidence indicates that the shape of nanoparticles has a big influence on health outcomes, but currently there are no ways to detect this property in isolation. This project will combine continuum fluid-mechanics models with probabilistic particle simulators to train a predictive tool capable of inferring shape, and other properties, from measurable quantities and limited experimental data. |
Continuum |
| Optical pump-probe spectroscopy is the go-to method for capturing ultrafast (femtosecond) dynamics in solid-state materials and has been used to understand everything from magnetism to photosynthesis. The problem is that pump-probe methods can only capture the ‘average’ dynamics in a system. This means that rapid fluctuations in electronic excitations, critical to phenomena like superconductivity, ion transport or (quantum) phase transitions, remain inaccessible. This project will revolutionise the information limits of pump-probe spectroscopy. We will build quantitative analytical/computational models of the experiment to derive new, universal, measurement and analysis protocols for capturing ultrafast nonequilibrium physics from pump-probe. We will then apply these methods to explore otherwise ‘hidden’ properties, e.g., quantum entanglement, in solid-state materials. The project is ideal for a student with interests in quantum dynamics, information theory/AI and stochastic physics, with close links to experimentalists. |
Quantum | |
| Aluminium-steel fusion welding: What happens at the interface? |
Aluminium and steel are widely employed metallic materials for automotive applications, such as in vehicle frames. Joining of these two dissimilar metals by fusion welding results in formation of brittle aluminium-iron compounds at the interface, which degrade the performance of the weld. Looking at the motion of individual atoms, we will use modelling techniques such as Molecular Dynamics (MD) and Kinetic Monte Carlo (KMC) combined with transmission electron microscopy analysis to study how iron and aluminium atoms react at the weld interface to find weld conditions where favourable intermetallic compounds form at interface. |
Atomistic |
| Machine learning accelerated design of composite materials for hydrogen economy |
Hydrogen is a zero-carbon emission fuel with the potential to decarbonise automotive and aerospace industries. Design of super-durable composite materials that can sustain harsh hydrogen environments is critical to achieving decarbonisation goals for the benefit of our planet. Multi-scale modelling methodologies that integrate modelling concepts from chemistry, physics, and engineering, and are accelerated with Machine Learning (ML), are crucial for accurate and efficient design of composites. This project will develop a radically-new predictive platform by combining mechanistic and data-driven approaches within the Bayesian framework. The platform will generate new knowledge and computer design tools, enabling wider exploitation of composite materials in hydrogen economy. |
Continuum |
| Unlocking their potential: Modelling Accelerated Degradation in Ni-rich Li-ion Batteries |
Electric vehicles employ Ni-rich layered oxides for their Li-ion batteries that offer high energy densities but also accelerated degradation. To avoid this degradation, <3/4 of the available lithium is used.To reach electric vehicle targets for the next decade,design strategies are needed to increase battery cycle lifetimes. Recent battery studies have revealed the Li-ions can get trapped behind atomically thin surface layers formed by the oxygen loss. Modelling the transport properties across these boundaries is critical for identifying and evaluating engineering solutions. This PhD project will have access to unique battery studies at Warwick to test their models. |
Continuum |
Filled Projects
| Project Title | Description | Keywords |
|---|---|---|
| Machine Learning Potentials for Strength Studies in Hexagonal Close Packed Materials |
In high-performance applications such as aerospace and medical technologies, there is a high demand for specialised materials with high strength-to-weight ratio and superior corrosion resistance. The reliability and improved development of these materials hinges on our atomic-level understanding on how they behave under stress or strain, and how defects in their crystalline structure affect their performance under different temperature-pressure conditions. This PhD project will take advantage of recent developments in machine learning methods, to enable computer modelling of the mechanical behaviour of titanium alloys to produce a machine learning-based interatomic potential and reference database, as well as to assess its performance in strengths applications. |
Atomistic |
| Space Weather is driven by large-scale eruptions from the Sun called coronal mass ejections. Upon arrival at the Earth, these produce amazing auroral displays but also endanger satellites and disrupt communication signals. Accurately modelling magnetosphere-atmosphere energy transfers is important to understanding the evolution of planetary atmospheres as well as developing real-world space weather forecasts. Through collaboration with QinetiQ, this project will develop state-of-the-art plasma simulations to probe magnetosphere-atmosphere interactions during solar storms, with application to characterising their technological impacts as well as to understanding Earth-like exoplanets subjected to different star-planet interactions. |
Continuum | |
| A Drug Delivery Challenge: Cracking |
Embark on a fascinating journey into the world of drug delivery and mass transport in complex biological systems. This project will explore how molecules navigate through disordered networks, from liposomes to human skin. Led by experts in computer simulations and continuum models, you will unravel the connections between molecular-level interactions and macroscopic drug delivery processes. You will develop skills in both atomistic simulations and fluid dynamics, while addressing challenges such as optimising drug transport and release via state-of-the-art nano-carriers. This project will leverage collaborations with experimental partners and offers a unique opportunity to make a real impact on healthcare applications. |
Atomistic |
| Artificial Intelligence driven multi-physics phase field fracture simulations for composites | Composites are widely adopted by automotive, aeronautical, and structural engineering due to their enhanced properties, yet their complex heterogeneous structure presents several challenges. Fracture is recognised as the main one, as it impacts composite safety, and when coupled with other physics, can lead to complex thermo-mechanical damage/failure scenarios. Commercially viable composite structures demand numerical methods adept at handling such complexities. This research aims to utilise the latest computational material modelling techniques to predict complex cracking patterns in composites, followed by creating an AI-driven multi-physics model for fast structural assessments. Outcomes will include enhanced understanding of damage processes, a new approach for investigating damage processes via phase-field fracture simulations, and a method to accelerate simulations using scientific machine learning. |
Continuum |
| Atomistic insight into nucleation and electrochemistry: Machine learning multiscale simulation | Developing battery technologies requires atomistic insight into electrochemistry, nucleation, and degradation, but simulation is presented with a challenging combination of lengthscale, timescale and accuracy demands. This presents a great opportunity for Scientific Machine Learning to work closely with experimental techniques such as transmission electron microscopy, and to learn to simulate nucleation and electrochemistry processes. In this project, we will use machine learned interatomic potentials to make simulated training data for ML models of nucleation. This will be paired with TEM imaging that captures atomic-level electrochemical processes in situ on 2D materials as they occur and constrains and informs our models. | Quantum |
| DRUG-THE-BUG: Determining druggable binding sites in bacterial membrane proteins |
The bacterial cell envelope is the front-line to killing drug-resistant, pathogenic bacteria. The development of new protein structure prediction methods (e.g. RoseTTA All-Atom AlphaFold and ESMFold) have enabled the accurate computational determination of over 600 million protein structures. This dataset enables the study of entire bacterial membrane proteomes from the perspective of structure-based drug discovery. As part of this Cresset-sponsored PhD studentship (https://www.cresset-group.com), you will develop and apply methods to identify and characterize binding pockets, predict candidate small molecule and antibody binding, and perform free energy calculations of molecules bound to folded protein structures. The overall aim of this PhD proposal is to develop blueprints for new medicines to treat drug-resistant bacterial infections. |
Atomistic |
| Frozen In: Predicting Microstructure in Solidifying Droplets |
This project is a pioneering study into the microstructural development inside spreading and solidifying droplets (Fig.1e), to solve 21st century challenges such as efficiency-reducing ice accretion on wind turbines (Fig.1f) and poor bonding in the 3D printing of metals (‘MetalJet’ Fig.1b,c). Guided by experts in the latest scientific techniques, you will predict the complex dendritic growth of crystals within a droplet (Fig.1a,d), connect this to engineering-scale mechanical properties and have the opportunity to apply machine learning image processing techniques to guide theory with experimental analyses. Your research will lead to new discoveries and a close interaction with our industrial collaborators. |
Continuum |
| Learning Collective Dynamics from Accelerated Quantum Jump Monte Carlo |
Future quantum technologies preparation and manipulation of quantum systems, but current setups can achieve only limited control. Improving on this requires modelling these experiments, but number of configurations and long timescales inhibits exact numerical approaches. We will circumvent the former by exploiting symmetries in collective open quantum dynamics for simulations of hybrid systems in quantum optics, and by modifying rare events techniques, we will avoid the latter. We will further use dimensional reduction to identify and model phase transitions in experiments. We aim to propose and verify new experimental settings that could support such phenomena. |
Quantum |
| The project aims to enhance Single Particle Analysis (SPA) software, a vital 3D imaging tool in structural biology, by integrating GPU-enhanced ptychography, an advanced computational microscopy technique, into a worldwide SPA framework. Traditional SPA techniques face limitations in resolving small and complex biological macromolecules. By incorporating ptychography, known for its high phase-sensitivity and applicability in low-dose data scenarios, researchers can achieve higher resolution in cryo-electron microscopy. This integration extends the utility of SPA, allowing researchers to work with diverse imaging modalities, significantly elevate data quality, and push the boundaries of resolution. Through international collaboration with leading experts and cutting-edge resources, we aim to advance structural biology, providing the potential for more precise macromolecular reconstructions and overcoming current challenges. |
Atomistic | |
| Unlocking Future Photovoltaics: the Effect of Interfaces on Ion Mobility in Perovskite Solar Cells |
Solar modules incorporating lead-halide perovskites now exceed the efficiency of conventional silicon modules. However, there is much still to be learned in order to drive further technology improvements. One area of interest is the presence of mobile ionic species, which have been shown to play a role in device efficiency, hysteresis and long-term stability. The aim of this Oxford PV-sponsored studentship is to investigate the physical and chemical consequences of mobile ions diffusing around interfaces, for example those at grain boundaries and contact layers. This project will make use of ab initio and machine learning techniques to provide in-depth insights that will help the continued development of state-of-the-art solar technologies. |
Atomistic |
Are you interesting in applying for any of these projects? Head over to our Study with Us page for information on the application process, funding, and the HetSys training programme
At the University of Warwick, we strongly value equity, diversity and inclusion, and HetSys will provide a healthy working environment, dedicated to outstanding scientific guidance, mentorship and personal development.
HetSys is proud to be a part of the Engineering Department which holds an Athena SWAN Silver award, a national initiative to promote gender equality for all staff and students.