PhD Project Opportunities 2023
Featured Project: Quantum Algorithms for Fusion Physics
Supervised by Animesh Datta and Tom Goffrey. PROJECT NOW FILLED
Nuclear fusion involves some of the most computationally demanding simulations in the physical sciences. They are too large to be performed even on the largest existing and forthcoming supercomputers.
This project, in collaboration with IBM Research, will develop quantum algorithms for efficient quantum simulation for fusion physics. Quantum computation promises efficient algorithms for problems that are classically inefficient to solve. This project will develop quantum algorithms to solve families of coupled partial differential equations.
Find out more...
For further guidance on how to apply, student funding, the intregrated HetSys training programme and what life is like in the HetSys CDT, please visit the Study with Us page.
April 2023: Please note that the application deadline for international candidates has now passed.
Project Title  Description  Keywords  Theme 


PROJECT FILLED 
Biomolecular modelling; Medicines  
Charting a course towards new lightactivated molecules Supervisors: 
PROJECT FILLED Many small molecules absorb light. Predicting what happens to them next is a challenging task for computer simulations, but if we could solve this problem we would have a new route to designing new fluorophores for medical diagnostic imaging, new photocatalysts for green synthesis, or new chromophores for harvesting the sunâ€™s energy in solar cells. In this project, we aim to address this challenge by combining excitedstate electronic structure calculations and machinelearning to build new predictive models to help us search the entire space of small organic molecules to identify useful (and previouslyunknown) lightactivated molecules. 
Materials characterisation; Electronic devices; Catalysis  
Overcoming scale and time: efficient simulations of collective open quantum systems Supervisors: 
PROJECT FILLED Emergent quantum phenomena such as nonclassical phase transitions arise from interplay between many components in large systems. Computer simulation of these phenomena is however restricted to small sizes. If distinct timescales also arise, the required length of simulations is equally problematic. We will circumvent the former by exploiting symmetries in collective open quantum dynamics for experimentally relevant simulations of quantum optics. We will avoid the latter by adapting rareevent simulation techniques to uncover dynamical aspects of dissipative quantum phase transitions. Results will guide the adaptation of a neural network ansatz to study more complex models. 
Quantum information; Mathematical and statistical foundations 
Quantum 
Probabilistic learning for timeparallel solvers of complex models Supervisors: Massimiliano Tamborrino (Statistics), Tim Sullivan (Mathematics\Engineering) 
Complex models in science often involve solving large systems of differential equations (DEs), whose study may be strongly limited by the wallclock time to numerically integrate them in time. For example, turbulent fusion plasma simulation can take 100200 days to integrate over a time interval of 1s. 
Mathematical and statistical foundations; Uncertainty quantification; fluid plasma simulation; timeparallel solvers; simulationbased schemes  Continuum 
Reuse of wastewater for agricultural irrigation Supervisors: 
Wastewater reuse for agricultural irrigation is an option to deal with water scarcity and associated threats to food security. Investigations show that treated wastewater may contain micro and nanopollutants. What is to be explored is the capacity of the soils to act as a filter for purifying wastewater from pollutants. Predicting the filtering capacity of the soils requires a firm scientific foundation for the characterization of basic mechanisms associated with reactive pollutant transport in porous media, and a robust understanding of all relevant processes and properties across the spectrum of relevant length and time scales. This project aims to develop and use machine learning algorithms that quantify the rate of interaction between the micropollutants and soil termed sorption rates. 
Mathematical and statistical foundations  Continuum 
Supervisors: 
PROJECT FILLED 
Materials characterisation; Mathematical and statistical foundations  Atomistic 
Blending ultrasound data with physicsbased models to predict damage in structural systems Supervisors: 
PROJECT FILLED 
Materials characterisation; Mathematical and statistical foundations; Alloys; Composites; datadriven computational modelling of materials  Continuum 
Developing the capability to forecast extreme Space Weather events Supervisors: 
PROJECT FILLED 
Mathematical and statistical foundations; space weather  Continuum 
Reverse engineering models for soft matter systems

Softmatter systems exhibit an extremely rich macroscopic behaviour, with complex and fascinating phase properties. Many of these exotic structures and transitions can be captured by relatively simple hardcore softshell interaction potentials in simulations, however, our precise understanding is often obscured by difficulties in sampling the configuration space exhaustively. Linking such potentials to specific macroscopic properties requires many expensive simulations, limiting insight into how the form of the potential determines phase behaviour. In this project we will adapt a novel dataintensive sampling algorithm to make automated predictions of structural and thermodynamic properties. We will then exploit the new algorithm to reverse engineer models, using machine learning methods, that capture novel phase behaviour of specific softmatter systems by design rather than discovery through bruteforce trial and error. Read more... 
Materials characterisation  Atomistic 
Machine learning multiscale simulation of photoconductivity in correlated oxides

PROJECT FILLED Predicting, explaining and modelling novel behaviours of quantum materials requires a combination of theoretical insight with stateoftheart multiscale modelling. In the case of complex oxides, displaying both strong electronic correlation and a diverse range of extended and point defects, traditional electronic structure methods encounter severe challenges when trying to model key properties such as photoconductivity and bulk photovoltaic effects. Fortunately, the extraordinary speed and power of machinelearned interatomic potentials provides a brandnew way to gain insight into these systems. This project will design and build multiscale models to understand photoconductivity in SrTiO3, particularly enhancement associated with dislocation cores. 
Materials characterisation; Electronic devices  Quantum 
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 wellstudied one) is the 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. 
Materials characterisation; Mathematical and statistical foundations  Continuum 
Alternative protein sources: growing the next generation computational modelling framework

PROJECT FILLED The alternative protein space represents one of the most dynamic scientific areas at present. Shrinking usable land mass, even accounting for agricultural advances, means a sustainable future is tightly linked with our ability to create and support clean food sources. Computational modelling has an immense (and largely untapped) potential to innovate a technological space still in its infancy. We will use a combination of stateoftheart techniques  multiphysics fluid mechanics, high performance computing and datadriven approaches  to design a versatile opensource modelling framework supported by fantastic experimental and industrial collaborators from around the world. 
Mathematical and statistical foundations; Smart fluids  Continuum 
Resisting the pressure: phasefield approach for composites under hydrogen environment

When exposed to pressurized gaseous environments, composite materials can exhibit microscale damage phenomena such as microcavitation. Understanding of those damage phenomena in the presence of tiny gas molecules such as H2 is critical for future applications of composites for H2 storage. Here, we aim to develop a new chemomechanical phase field model that will predict onset and propagation of microscale damage as a function of material composition, hydrogen concentration/pressure, and loading conditions. The model will be experimentally informed (model parameters, microstructure) using a Bayesian approach. 
Composites  Continuum 
Complex electronic structures for thermoelectric energy materials

PROJECT FILLED Two thirds of all energy used is lost into heat during conversion processes, which puts enormous pressure on energy sustainability. Thermoelectric materials convert waste heat into electricity and can provide solutions towards this problem. Recently, a myriad of materials and compounds with complex electronic structures have been synthesized, offering possibilities for exceptional thermoelectric performance. The project uses Density Functional Theory coupled to advanced electronic transport methods, to examine the potential of the most prominent materials, targeting appropriate electronic structure designs for further optimization. The richness of experimental data, both from literature and in house, will aid towards theory validation. 
Materials characterisation; Alloys  Quantum 
Reliable quantum algorithms for plasma and fusion physics

PROJECT FILLED The field of quantum computation and simulation seeks to develop efficient quantum algorithms for problems that are classically inefficient to solve and are therefore computationally expensive. Furthermore, a quantumenhanced simulation must not only perform a hard classical simulation efficiently, but also correctly. The latter goal is particularly important as realworld quantum computers are noisy and error prone. This project, in collaboration with IBM Research, will develop algorithms for efficient quantum simulation for plasma and fusion physics problems, and establishing their reliability in realworld quantum computers. The project is ideal for a student interested in a close interplay of quantum computation and simulation with plasma physics. 
Quantum Information  Quantum 
Multiscale Modeling of Mass Transport in Disordered Media: from Drug Delivery to Freezing Tolerance

Understanding how molecules move through disordered networks is a fundamental question that has an impact on countless practical applications. For instance, many medicinal drugs are delivered through our skin  a very complex system. Another example is the percolation of ice through plant cells, which can massively reduce crop yield. Until now, the modelling of these applications has relied on either continuum fluid dynamics or molecular simulations. With this project, we will build a bridge between the two disciplines, thus developing an understanding of the relationship between (macroscopic) mass transport and the (microscopic) structure of biological disordered materials. 
Biomolecular modelling; Materials characterisation; Mathematical and statistical foundations; Medicines  Atomistic 
Machinelearning quantum surrogate models to simulate energy transport across interfaces

PROJECT FILLED Modern technologies such as photocatalysis or laser nanolithography involve energy transfer across interfaces. Many critical societal challenges require that we transfer light or electronic energy more efficiently into chemical energy, e.g., to utilize CO_{2} as renewable fuel. To achieve this, we need to understand the mechanisms behind the intricate dynamics that unfold at interfaces. Quantum mechanical simulations provide electronicstructure insights but are computationally intractable for relevant systems. The aim of this project is to create and apply machine learning models that emulate the quantum mechanical interaction of light, electrons, and atoms for many thousands of atoms at realistic interfaces. 
Mathematical and statistical foundations; Catalysis; Alloys  Quantum 
What's that made of? Modelling muonic Xray radiation for quantitative elemental analysis Supervisors: Nicholas Hine (Physics), Albert BartokPartay (Engineering\Physics)

PROJECT FILLED 
Quantum 