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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.

Filled Projects

Project Title Description Keywords

A Drug Delivery Challenge: Cracking
the Code of Mass Transport in Disordered Systems

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.

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.

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 (, 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.


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.


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.


Pushing the boundaries of resolution of biological objects by electron microscopy with GPU-enhanced ptychography

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.


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.


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.


Are you interesting in applying for this project? 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.