Highlights of our PhD projects currently recruiting
We've highlighted some of our PhD projects currently open for UK applicants below.
HetSys is built around a closely knit, highly collaborative team of academics from five science departments at Warwick with a strong track record in leading large projects.
Click on the supervisors for more details of our wonderful team or visit our people pages to see what our other student cohorts are working on.
To see all of our projects currently recruiting UK applicants click here.
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.
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
Alpha energy deposition simulations for direct-drive laser fusion
The recent successes at the National Ignition Facility in achieving fusion gain from laser-driven implosions is major step forward for fusion research. For UK based researchers the next step is to work towards a direct-driven laser fusion facility. Such a step requires predictive models for laser system and fuel pellet design. Such codes are also needed now to interpret existing experimental data.
Warwick has developed a dedicated fluid code for this work but it is missing some key physics packages. The code needs to include fusion reactions and alpha particle energy deposition. This involves coupling particle based MC codes to the fluid code with quantifiable estimates of uncertainty. The project is part funded and supervised by the Central Laser Facility at the Rutherford laboratory and is part of a much wider UK coordinated fusion programme.
Supervisors
Dr. Tom Goffrey
Prof. Tony Arber
Supervisors
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.
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.
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.