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PhD Project Opportunities 2023

Available Projects 2023/24

We are pleased to announce that our projects to starting in the 2023-24 academic year are open for applications. We aim to offer 10 fully funded PhD positions (fees, stipend and generous research support and training grant). Please see below for details of the projects available.

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.

Project Title Description Keywords Theme

Identifying druggable binding sites in computationally-determined models of bacterial membrane proteins


Supervisors:
Phillip Stansfeld (Life Sciences\Chemistry), Livia Bartok-Partay (Chemistry)

The bacterial cell envelope is the front-line to killing drug-resistant, pathogenic bacteria. The development of AlphaFold2 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 proposal, we aim to develop and apply methods to identify and characterize binding pockets, dock candidate small molecules and perform free energy calculations to investigate small molecule binding to folded protein structures. Our overall aim is to develop blueprints for new medicines to treat drug-resistant bacterial infections.

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Biomolecular modelling; Medicines

Atomistic

Charting a course towards new light-activated molecules

Supervisors:
Scott Habershon (Chemistry), Nicholas Hine (Physics), Albert Bartok-Partay (Engineering\Physics)

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 sun’s energy in solar cells. In this project, we aim to address this challenge by combining excited-state electronic structure calculations and machine-learning to build new predictive models to help us search the entire space of small organic molecules to identify useful (and previously-unknown) light-activated molecules.

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Materials characterisation; Electronic devices; Catalysis

Quantum

Overcoming scale and time: efficient simulations of collective open quantum systems

Supervisors:
Katarzyna Macieszczak (Physics), David Quigley (Physics)

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 rare-event 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.

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Quantum information; Mathematical and statistical foundations

Quantum
Probabilistic learning for time-parallel 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 100-200 days to integrate over a time interval of 1s.
To tackle this, time-parallel integration methods have been proposed, but they: 1) do not account for the underlying uncertainty (e.g. model misspecification, numerical or observation errors); 2) do not scale well for high-dimensional DEs; 3) have not yet been embedded within parameter estimation algorithms. This PhD project aims to fill in one or more of these gaps.

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Mathematical and statistical foundations; Uncertainty quantification; fluid plasma simulation; time-parallel solvers; simulation-based schemes Continuum

Reuse of wastewater for agricultural irrigation

Supervisors:
Mohad Nezhad (Engineering), Gary Bending (Life Sciences)

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 nano-pollutants. 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.

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Mathematical and statistical foundations Continuum

(Inter)facing the Bitter Truth: How to Design Better Interfaces in Next-Gen Batteries using Atomistic Simulations Assisted by Machine-Learning

Supervisors:
Bora Karasulu (Chemistry), Albert Bartok-Partay (Engineering\Physics)

Lithium-Sulphur batteries (LSBs) are a promising alternative to Li-ion batteries (LIBs) as a next-gen energy storage technology, providing higher theoretical capacity at lower costs. Replacing the conventional liquid electrolytes with solid electrolytes (SE) helps mitigate the major LSB issues like the Li-polysulfide shuttle effect, and safety risks. Current SEs, however, degrade when coupled with a S-cathode, impeding the Li-ion conduction across their interfaces, limiting the battery performance. To design superior SE/S-cathode interfaces, this project focuses on atomistic simulations of the interfacial sulphide conversion chemistry in LSBs utilising state-of-the-art Density Functional Theory and machine learning methods, providing insights that are otherwise elusive to experimental characterisation techniques.

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Materials characterisation; Mathematical and statistical foundations Atomistic

Blending ultrasound data with physics-based models to predict damage in structural systems

Supervisors:
Emmanouil Kakouris (Engineering), Rachel Edwards (Physics), Peter Brommer (Engineering)

Monitoring of damage degradation in materials is vital as any cracks will reduce local stiffness, accelerating the ageing process of the physical assets. In-situ ultrasonic monitoring data is valuable for giving the inspectors information on the level of what may be wrong with the structural systems. However, purely data-driven representations do not always give inspectors enough information and the ability to predict how the system will behave in the future. This can be achieved by utilising some knowledge of the physics that underpins the system. This project will use the fusion of experimental data and physical models for failure prediction of materials, by taking measurements and developing models that are hybrid in nature.

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Materials characterisation; Mathematical and statistical foundations; Alloys; Composites; data-driven computational modelling of materials Continuum

Developing the capability to forecast extreme Space Weather events

Supervisors:
Ravindra Desai (Physics), Jeremie Houssineau (Statistics), David Jackson (UK MET Office)

Extreme 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 astronauts, and disrupt communications and power grids. Forecasting these events is of primary importance to the UK MET Office, one of three centres world-wide providing round-the-clock space weather predictions. This project, in collaboration with the UK MET Office, will develop state-of-the-art plasma simulations to forecast extreme Space Weather and develop advanced statistical techniques to quantify the uncertainties in their prediction.

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Mathematical and statistical foundations; space weather Continuum

Reverse engineering models for soft matter systems


Supervisors:
Livia Bartok-Partay (Chemistry), David Quigley (Physics), James Kermode (Engineering)

Soft-matter 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 hard-core soft-shell 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 data-intensive 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 soft-matter systems by design rather than discovery through brute-force trial and error.


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Materials characterisation Atomistic

Machine learning multiscale simulation of photoconductivity in correlated oxides


Supervisors:
Nicholas Hine (Physics), Reinhard Maurer (Chemistry\Physics), Marin Alexe (Physics)

Predicting, explaining and modelling novel behaviours of quantum materials requires a combination of theoretical insight with state-of-the-art 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 machine-learned interatomic potentials provides a brand-new 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.

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Materials characterisation; Electronic devices Quantum

Building better batteries: modelling and optimisation of electrode filling


Supervisors:
Ferran Brosa Planella (Maths), Radu Cimpeanu (Maths)

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 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.

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Materials characterisation; Mathematical and statistical foundations Continuum

Alternative protein sources: growing the next generation computational modelling framework


Supervisors:
Radu Cimpeanu (Maths), Ferran Brosa Planella (Maths)

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 state-of-the-art techniques - multi-physics fluid mechanics, high performance computing and data-driven approaches - to design a versatile open-source modelling framework supported by fantastic experimental and industrial collaborators from around the world.

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Mathematical and statistical foundations; Smart fluids Continuum

Resisting the pressure: phase-field approach for composites under hydrogen environment


Supervisors:
Lukasz Figiel (WMG), Mohad Mousavi-Nezhad (Engineering)

When exposed to pressurized gaseous environments, composite materials can exhibit microscale damage phenomena such as micro-cavitation. 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 chemo-mechanical 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.

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Composites Continuum

Complex electronic structures for thermoelectric energy materials


Supervisors:
Neophytos Neophytou (Engineering), Gavin Bell (Physics)

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.

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Materials characterisation; Alloys Quantum

Reliable quantum algorithms for plasma and fusion physics


Supervisors:
Animesh Datta (Physics), Tom Goffrey (Physics)

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 quantum-enhanced simulation must not only perform a hard classical simulation efficiently, but also correctly. The latter goal is particularly important as real-world 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 real-world quantum computers. The project is ideal for a student interested in a close interplay of quantum computation and simulation with plasma physics.

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Quantum Information Quantum

Multiscale Modeling of Mass Transport in Disordered Media: from Drug Delivery to Freezing Tolerance


Supervisors:
Gabriele Sosso (Chemistry), James Sprittles (Maths)

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.

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Biomolecular modelling; Materials characterisation; Mathematical and statistical foundations; Medicines Atomistic

Machine-learning quantum surrogate models to simulate energy transport across interfaces


Supervisors:
Reinhard Maurer (Chemistry), James Kermode (Engineering)

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 CO2 as renewable fuel. To achieve this, we need to understand the mechanisms behind the intricate dynamics that unfold at interfaces. Quantum mechanical simulations provide electronic-structure 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.

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Mathematical and statistical foundations; Catalysis; Alloys Quantum