Join us

Dr Artur Gower

Supervisor

A funded PhD scholarship is available to develop sensors for ultra thin membranes supervised by Dr Art Gower (https://arturgower.github.io/), and industrial supervisors Dr Carl Tipton (principal measurement engineer) and Dr Alessandro Abena. The aim is to develop and design new sensors for the digital revolution in industry together with Johnson-Matthey, a science based company.

Background: Ultra-thin membranes are produced in many high tech industries. They are the basis of flexible solar panels and electronics, as well as biosensors for medical diagnostics.

To automate the production of these membranes we need a real-time virtual representation of them. The focus of this project is to create a new sensor for these membranes. You will need to develop a method to create small elastic waves along them, and then measure these waves with lasers or ultrasonics methods. The speed and amplitude of the waves are key to understanding the composition and defects in these membranes. Currently there is a significant amount of waste produced due to manufacturing defects, these sensors can be used to understand the causes and better automate production processes.

You will be on a team including industrial and academic supervisors who will support you. You will lead the experimental work, as well as develop knowledge on machine learning methods. There will also be an opportunity to learn about mathematical modelling. Additionally, you will be part of a world leading Dynamics group at the University of Sheffield.

Prof David Lidzey

Perovskite solar cells (PSCs) represent one of the most significant breakthroughs in materials science over the last decade. These synthetic materials, offer light-absorption properties and charge-carrier mobilities that now rival conventional silicon. However, the primary barrier to their global adoption is the “scale-up gap.” While small-scale lab devices show exceptional efficiency, maintaining that performance during high-speed, industrial manufacturing remains a significant challenge.

This PhD project focuses on the transition of PSCs from the lab to commercial-scale production. In collaboration with Power Roll Ltd, you will work on a unique architecture: their v-groove, back-contact device technology. Unlike standard flat-panel solar cells, Power Roll’s design uses thousands of micro-grooves in which each groove acts as an individual solar-cell device [1,2]. While this design is innovative, cost-effective and eliminates expensive materials, it introduces additional physical requirements during the device fabrication process.

This project is based around roll-to-roll (R2R) slot-die coating; a process similar to newspaper printing but requiring nanometer-level precision. At industrial speeds, the quality of the perovskite film is dictated by an intricate web of variables, including, ink rheology (how the liquid precursor flows under pressure), film drying kinetics (the rapid transition from a wet film to a solid crystal) and fluctuations in local temperature, web speed, and pump pressure etc. Because these variables interact in non-linear ways, traditional “first-principles” models often struggle to predict the outcome. Your task will be to integrate a suite of optical characterization tools (e.g. measuring absorption and photoluminescence) directly into a prototype R2R printing line. This “on the fly” collected-data, combined with structural analysis like scanning electron microscopy, will form the basis for Machine Learning models. The ultimate goal is to move beyond trial-and-error, creating an autonomous system capable of identifying the optimal printing conditions to maximize efficiency and yield.

You will be supervised by Prof David Lidzey at the University of Sheffield, School of Mathematical and Physical Sciences. Prof Lidzey is an internationally recognized leader in the field of organic and hybrid electronics, and leads an active, friendly and supportive research group working on perovskites and other thin-film optoelectronic technologies. More details about our group can be found here https://epmm.sites.sheffield.ac.uk.

We are seeking a candidate with a background in Physics, Physical-Chemistry, Materials Science, or Engineering. You should have an interest in experimental “hands-on” research. As part of the project you will work on data analysis and the application of Machine Learning (ML) to physical systems in conjunction with the group of Prof George Panoutsos (https://sheffield.ac.uk/eee/george-panoutsosy-test), and so experience or interest in ML / data science will be an advantage. By joining this project, you will work at the interface of a world-class academic institution and a pioneering UK company, gaining a unique set of skills in both advanced materials and Industry 4.0 manufacturing.

Prof Jonathan Howse

Slot-die coating is the premier technique for the continuous production of high-value functional thin films – the literal building blocks of next-generation photovoltaics, biosensors, and energy storage systems. However, traditional methods are ‘passive’ and ‘open-loop’. Once the parameters are set, the system is left to the mercy of natural physical processes, often resulting in defects like “coffee-ring” effects, uneven drying resulting in non-optimum coatings and poor device performances.

Currently, materials science is moving so fast that we are developing ‘hard-to-coat’ inks that are functionally superior but practically ‘un-printable’ due to unstable rheology. We don’t want to settle for easier-to-process materials; we want to build a system smart enough to handle the difficult ones.

In this project, you will move beyond passive deposition into the realm of ‘Active Slot-Die Coating’. You will integrate real-time sensors and external stimuli – specifically electric fields and active flow control – into an automated coating line.

The primary hurdle isn’t just the hardware; it’s the intelligence behind the control. Your role will be to:

• Engineer ML Control Algorithms: Develop AI models that act as a “digital twin,” interpreting multi-fidelity sensor signals to identify priority feedback for live, autonomous adjustments.
• Smart Metrology: Design “information-rich” imaging systems that move characterization from a “post-mortem” analysis to a live, in-situ process.
• Bridge the Gap: Use ML to bridge the gap between hydrodynamic simulations and the “noise” of real-world manufacturing.

This research is grounded in commercial reality through an industrial partnership with FOM Technologies (Denmark). As part of your training, you will:

• Attend the FOM Coating School to master the physics of Roll-to-Roll (R2R) processing.
• Gain access to specialized R2R equipment at Echion Technologies in Cambridge for rapid prototyping.
• Go Global: This project includes a proposed 3-6 month sabbatical at Argonne National Labs (USA), working with a consortium of US national laboratories on high-TRL energy challenges.

As a member of the Materials 4.0 CDT, you will receive a comprehensive suite of academic and practical training:

• Advanced Modules (@ Uni Sheffield) in subjects like Computational Intelligence (ELE428), Data Modelling (ELE448), and Industrial Automation (ELE426).
• Specialized Skills: Develop expertise in LabVIEW I/O systems for bespoke instrumental control, on-line metrology, and COMSOL Multiphysics for digital twin modelling.

Who are we looking for?

This project would suit someone from an Physical Science and Engineering background and your application is encouraged. We are particularly keen to receive applications from candidates with an enthusiasm for Machine Learning, Computer Science, or Control Systems Engineering. It is important to note you don’t need to be an expert in thin film processing on Day 1, but you must be:

• Curious and Engaging: We want someone who is ready to dive into the “physics” of the problem while bringing their computational expertise to the table.
• Willing to Learn: You will be supported in mastering the technical and engineering aspects of slot-die coating and thin-film deposition via in-house knowledge and extensive interactions and training from the industrial partner.
• Interdisciplinary: You will be part of a world-class supervisory team, including Prof. Jonathan Howse (Coating Trials), Dr. Morgan Jones, and Prof. George Panoutsos (ML/AI and Computational Intelligence).

Dr Lewis Owen

Supervisor

Total scattering is an advanced X-ray and Neutron scattering technique that provides atomic scale information about the structure of a material. Many important materials systems are known to have properties that are strongly affected by this local structure (effects on the atomic scale) including materials for atomic energy, battery components and structural applications. For example, short-range order (the preference of atoms to sit next to or avoid each other) can affect the radiation damage tolerance, electrical resistivity or strengthening properties of a material.

However, despite the importance of local structure and short-range order, studies using total scattering remain limited – particularly in metallurgical systems. One significant reason for this is the complexity involved in the data processing methods employed, and the subsequent analysis and fitting of the data. These steps involve expertise in multiple software suites and an understanding of the interdependence between them. This creates a barrier for novel users of the technique, preventing wide adoption of the method.

This project aims to lower the barrier by creating tools to aid in the analytical workflow. We will look to create novel tools and a coherent workflow that combine the diverse pieces of software used. This will provide a tool to guide the user through the steps of the process, allowing for information transfer between the software, and improving the quality of the resultant analysis. This will also involve the collection of a curated data set, and the use of Machine learning tools to further enhance the analytical process. Together, this will help expand the use of the technique and unlock new areas of materials exploration.

The project will involve a mixture of coding, data curation, practical experiments at UK national X-ray and Neutron facilities, and method development. This project would suit someone with a background in Material Science, Chemistry, Physics or Computer Science. The student will be part of the MOSAIC group at the University of Sheffield. The project will involve close work with colleagues at the ISIS Neutron and Muon Source and Diamond Light Source (the UK national research facilities), and the STFC Scientific Computing team. The student will also be part of the Royce Institute Materials 4.0 CDT and be part of a national cohort working to realise the potential of the digital and data revolutions in materials science. CDT students undertake a doctorate with an in-depth technical and professional skills training across a structured 4-year programme.

Prof David Fletcher

Supervisor

The UK’s leadership in high-value manufacturing sectors, from aerospace to energy, depends on the rapid development of advanced materials. However, a significant bottleneck hinders progress: the current timeline for moving a novel alloy from initial concept to industrial consideration often takes years, stifling innovation. This PhD project is designed to directly address this critical challenge. It will establish, validate, and utilise a high-throughput methodology to bridge this gap, demonstrating a development cycle of just a few months. This work, conducted in close collaboration with Rolls-Royce plc , will use a new Ni-based superalloy as the primary demonstrator system to create a new paradigm for accelerated materials discovery.

This program is at the very heart of the Materials 4.0 initiative. It replaces the traditional, slow, trial-and-error approach with a data-centric and agile workflow. The project integrates three core pillars: (1) Digital & Data-Driven Discovery, by creating a large, unique dataset linking composition to phase stability and fundamental mechanical properties for data-driven down-selection; (2) High-Throughput Automation, leveraging rapid manufacturing via Directed Energy Deposition (DED) and coupling this with a rapid characterisation pipeline (e.g., X-ray diffraction, thermal analysis, automated nanoindentation, high-throughput micro-plastometry) ; and (3) Accelerated Validation, by rapidly screening the top candidates across three distinct, industrially relevant manufacturing pathways (Laser Powder Bed Fusion, Hot Isostatic Pressing, and Field Assisted Sintering).

The initial methodology will serve as the core iterative cycle for this PhD. The student will run multiple, increasingly complex cycles to not only discover new alloys but also to refine the methodology itself.

Year 1: Methodology Validation
The student will execute the foundational FAST-TRACK cycle: Explore compositional space using CALPHAD and simple ML models. Manufacture a materials library and begin the high-throughput characterisation pipeline. Complete characterisation and perform data analysis to identify structure-property relationships and down-select candidates. Conduct rapid manufacturing assessment, involving powder atomisation and consolidation via LPBF, HIP, and FAST. Additionally, dedicated in-depth analysis of this dataset, presenting initial findings, and planning the second cycle will be carried out.

Year 2: Methodology Refinement & ML Integration
A second, more ambitious cycle will be executed. This will involve a more complex alloy system as well as refining and automating the characterisation protocols for even faster screening. A key focus will be integrating machine learning (ML) models, trained on the Year 1 dataset, to predict promising compositions, making Cycle 2 more targeted and efficient.

Year 3: Application & Physical Metallurgy Insights
The student will run a third cycle, applying the now-refined methodology to address a specific, complex industrial challenge posed by Rolls-Royce. This year will also involve deeper scientific investigation into the fundamental structure-property relationships for Ni-based superalloys at a much broader scale, with manufacturing integrated on a fundamental level thus moving beyond rapid screening to generate new understanding.

Year 4: Open Access Knowledge Transfer
The final year will be dedicated to completing all experimental work, final data analysis, and writing the PhD thesis. A critical component of this project is its commitment to the Materials 4.0 ethos. All new data analysis protocols, computational models, and the validated methodology will be prepared for open access publication. This ensures the final report and data packages provide a robust, transferable blueprint for applying this agile approach to other alloy systems, accelerating knowledge creation and solidifying the UK’s position as a leader in industrial innovation.

Dr Nicole Hondow

Supervisor

Electron microscopy is often used in the characterisation of nanomaterials, with scanning electron microscopy (SEM) and transmission electron microscopy (TEM) covering a wide range of nano- and micro-length scales, and associated spectroscopies providing chemical information. The ability to image and undertake analysis (size, structure, composition) at the individual particle level is unmatched by other techniques.

The challenge is that electron microscopy is disadvantaged by limited field of view and it being a manual approach. This results in a limited amount of data produced, often with a single “representative” image or data set being used to support characterisation from bulk techniques. A source of bias is introduced as a consequence of it being a manual approach.

The aim of this project is to create automated workflows for representative nanomaterial characterisation, encompassing data collection and subsequent data analysis. This research will challenge current approaches to electron microscopy with a view to establishing the potential for it to be a high-throughput technique with reduced bias.

The initial focus will be on nanomaterials, as electron microscopy is frequently used for size measurements and the immediate impact of automation approaches will be significant. The effect of different sizes will be assessed in terms of the instrumentation used, SEM compared to TEM, utilising the automation processes developed. The multitude of signals available via electron microscopy within imaging, spectroscopy and diffraction will require the development of a range of automation procedures, analysis workflows and data-centric interpretations. Critically, the sample preparation will be optimised utilising different approaches.

By harnessing advanced computer vision and artificial intelligence (AI) techniques, combined with automated cloud-based workflows, large-scale analysis of nanomaterials becomes feasible – enabling the generation of richly annotated datasets for detailed material characterisation. Generative AI can be employed to produce textual summaries of sample scans, allowing researchers to gain high-level insights across broad, representative samples before exploring specific areas of interest. Through the application of sophisticated computer vision algorithms, researchers can rapidly automate statistical analyses, delivering granular metrics on particle size, clustering, composition, and structural properties. This data-driven approach, powered by large-scale, unbiased sample collection, significantly reduces sample bias, improves reproducibility, and supports more robust, scalable insights into nanomaterial properties and behaviour.

The researcher will be trained on the world leading electron microscopy facilities in The Bragg Centre for Materials Research and will be part of the Leeds Electron Microscopy and Spectroscopy (LEMAS) group. Project progress will be accelerated through the researcher being trained in the use of the new instrumentation including the Tescan Amber X Focused Ion Beam Scanning Electron Microscope, equipped with both EDX and TOF-SIMS, and the Tescan Tensor, set up for 4D STEM analysis.

Dr Shufan Yang

Supervisor

This PhD Project sets the foundation for a new class of smart, durable hydrogel coatings designed using digital-first principles. By fusing materials science, machine learning, and advanced simulation, the project offers an exciting opportunity to redefine how we engineer and deploy functional surfaces for next-generation biomedical and medical implants. The project is supported by the University of Leeds, the University of Strathclyde and CN Technology Service Ltd.

Hydrogel coatings represent a critical interface technology for biomedical applications, yet their widespread adoption is severely limited by fundamental challenges in adhesion durability and mechanical integrity under real-world cyclic loading conditions. Structural limitations in current laboratory tests mean that hydrogel-coated prototypes cannot always be mechanically tested, complicating long-term performance evaluation for commercial development.

This project proposes developing an integrated digital twin platform that bridges experimental characterisation, multi-physics modelling, and deep learning to enable predictive design and optimization of hydrogel coating systems. The innovation lies in establishing a closed-loop workflow specifically for interfacial mechanics, with multi-scale integration and physics-informed machine learning and experimental design optimisation.

You will gain interdisciplinary skills in machine learning, medical image analysis, and coating design. As a PhD student within university of Leeds and co-supervised by staff at University of Strathclyde and CN Technology Service Ltd, there will be opportunities to contribute to wider activities related to precision measurement and transferable skill training. Groups of researchers working on aligned projects or using similar methods meet regularly to share ideas and best practice. We will support student long-term career ambitions through bespoke training and encourage external secondments, laboratory visits or participation at international conferences.

Candidates should hold a good bachelor’s degree (first or upper second-class honours degree) or an MEng/MSc degree in a relevant engineering/science subject.

Dr Josh Owens

Supervisor

Additive Manufacturing (AM) offers major advantages in design flexibility and material efficiency, but surface texture remains a key barrier to wider adoption, especially for corrosion-critical applications. This project in collaboration with Holdson, specialists in sustainable electropolishing for aerospace, medical, and renewable energy sectors, aims to develop and optimise a novel electropolishing process tailored for AM metal alloys to minimise corrosion.

The initial phase of the project will evaluate corrosion performance of stainless steel produced via Laser Powder Bed Fusion, comparing various surface treatment approaches. Corrosion testing will replicate harsh conditions relevant to Holdson’s applications. A core innovation of this project is the development of high-throughput corrosion screening methods, enabled by multi-material components produced through advanced AM techniques. This approach enables batch electropolishing and rapid electrochemical testing at specific locations, correlating local alloy composition and microstructure with corrosion behaviour. Advanced microscopy will provide detailed surface and material analysis. Finally, the project will use high-throughput data to build a machine learning framework that predicts optimal electropolishing parameters for AM metal alloys, to support industrial implementation by Holdson.

Dr David Chapman

This project will investigate how titanium alloys behave under the extreme thermo-mechanical conditions experienced during linear friction welding (LFW), a critical manufacturing process used in high-value aerospace components. During rapid heating, kinetic processes in titanium alloys cannot fully keep pace with the imposed thermal cycle, resulting in retention of lower-temperature strengthening features at temperatures where equilibrium would predict a significantly softer material state. This leads to substantially higher flow stresses than expected under equilibrium conditions and strongly influences deformation and weld behaviour. Despite the widespread industrial use of LFW, these transient material states remain poorly understood, limiting predictive modelling and constraining the introduction of new materials and process conditions.

The project will develop a semi-automated experimental capability for thermo-mechanical testing under process-representative conditions, combining advanced deformation testing, rapid heating, and full-field diagnostics such as digital image correlation (DIC) and thermography. This will enable systematic exploration of material response across a wide range of temperatures, strain rates, and thermal histories, while generating high-quality datasets for predictive modelling and validation.

Detailed microstructural characterisation will be used to investigate the relationship between thermal history, phase evolution, and flow stress. The project will involve close collaboration with Rolls-Royce and academic partners working on multiscale modelling of welding processes, providing the opportunity to contribute directly to the development of predictive manufacturing approaches for aerospace applications.

The successful student will receive training in high-temperature materials testing, experimental mechanics, advanced diagnostics, and materials characterisation, with opportunities for industrial collaboration, specialist external training, and potential synchrotron-based experiments.

This project is ideally suited to students with interests in materials science, mechanical engineering, experimental mechanics, manufacturing, or high-temperature material behaviour, particularly those interested in combining advanced experimental research with real industrial impact.

Prof Edmund Tarleton

Supervisor

This project will study how temperature gradients drive hydrogen diffusion. This little-studied effect, called thermomigration, can strongly impact hydrogen embrittlement. It is especially important in systems with steep thermal gradients, such as hydrogen fuel systems, nuclear fuel cladding and fusion reactor armour. This project will develop a machine-learning-enhanced digital material twin to correctly capture this effect, leveraging new thermo migration measurements.

The transport of solutes through metals due to a temperature gradient is known as thermomigration. It is important for structural metals exposed to hydrogen (H), as H embrittlement and hydride precipitation are sensitive to local concentration. To correctly predict H transport, especially in the presence of steep temperature gradients, thermomigration must be accounted for.

In reactor fuel cladding, steep temperature gradients arise radially from internal heating and water-side cooling. Thermomigration is likely to dominate the H concentration profile. For high fidelity modelling of H and hydride embrittlement in cladding materials this effect must be properly understood. Unfortunately, there is a lack of physical clarity regarding of the driving force(s) for thermomigration, including the complex associated electronic effects. The development of new modelling capabilities is urgently needed. These in turn require reliable experimental data across broad temperature and temperature gradient ranges.

The heat of transport, 𝑄∗, is used to quantify the direction and magnitude of thermomigration. Surprisingly, there is little experimental 𝑄∗data, with the most prominent examples found in Zr cladding alloy [1-3]. However, this data was attained post hoc by measuring H content at ambient temperature [4], introducing considerable uncertainties. The most robust heat of transport measurements were reported by Gonzalez and Oriani in the 1960’s for pure Fe and pure Ni, using a thermo-osmosis technique to measure 𝑄∗ in the 400 − 600 °C range [5].

The central goal of this project is to develop a digital material twin that accurately captures hydrogen transport and trapping in Zr under complex conditions (stress, temperature, hydrogen, irradiation etc.). To support this, a new rig will be constructed to allow robust 𝑄∗ measurements across broad temperature ranges. The rig will consist of a permeation cell with precise temperature gradient control across thin membrane samples, coupled with a mass spectrometer for highly sensitive H flux measurements. A detailed digital twin of the experiment will be constructed to allow rapid inversion of experimental measurements into Q* data.

The new heat-of-transport data will allow the student to test a physically-based model we recently proposed to capture the temperature dependence of heat-of-transport [6], and to calibrate this model for Zr. A key challenge will be to develop approaches to capture and explain differences between the model predictions and the experimental measurement, for example due to the transient interplay between H thermomigration and hydride precipitation at low temperatures, or evolution of the microstructure. A key complication is that our previous results suggest that molecular dynamics simulations are unlikely to accurately capture thermomigration due to the lack of electronic effects. Here, we propose to leverage the use of machine learning descriptors of atomistic configurations as a proxy to predict evolution in heat-of-transport (similar to an approach we developed to predict thermal conductivity from MD simulations of defective metals).

To maximise impact, the “thermomigration” digital material twin developed in this project will be integrated into the comprehensive material model being continuously developed by Rolls-Royce for fuel cladding design and optimisation. The combination of sophisticated newexperiments with cutting-edge material simulation and surrogate modelling to tackle a complexmaterials challenge with direct industrial impact strongly reflects the core motivation of the Materials 4.0 CDT.

Prof Felix Hofmann

Supervisor

This project will study how temperature gradients drive hydrogen diffusion. This little-studied effect, called thermomigration, can strongly impact hydrogen embrittlement. It is especially important in systems with steep thermal gradients, such as hydrogen fuel systems, nuclear fuel cladding and fusion reactor armour. This project will develop a machine-learning-enhanced digital material twin to correctly capture this effect, leveraging new thermo-migration measurements.

The transport of solutes through metals due to a temperature gradient is known as thermomigration. It is important for structural metals exposed to hydrogen (H), as H embrittlement and hydride precipitation are sensitive to local concentration. To correctly predict H transport, especially in the presence of steep temperature gradients, thermomigration must be
accounted for.

In reactor fuel cladding, steep temperature gradients arise radially from internal heating and water-side cooling. Thermomigration is likely to dominate the H concentration profile. For high fidelity modelling of H and hydride embrittlement in cladding materials this effect must be properly understood. Unfortunately, there is a lack of physical clarity regarding of the driving force(s) for thermomigration, including the complex associated electronic effects. The development of new modelling capabilities is urgently needed. These in turn require reliable experimental data across broad temperature and temperature gradient ranges.

The heat of transport, 𝑄∗, is used to quantify the direction and magnitude of thermomigration. Surprisingly, there is little experimental 𝑄∗data, with the most prominent examples found in Zr cladding alloy [1-3]. However, this data was attained post hoc by measuring H content at ambient temperature [4], introducing considerable uncertainties. The most robust heat of transport measurements were reported by Gonzalez and Oriani in the 1960’s for pure Fe and pure Ni, using a thermo-osmosis technique to measure 𝑄∗ in the 400 − 600 °C range [5].

The central goal of this project is to develop a digital material twin that accurately captures hydrogen transport and trapping in Zr under complex conditions (stress, temperature, hydrogen, irradiation etc.). To support this, a new rig will be constructed to allow robust 𝑄∗ measurements across broad temperature ranges. The rig will consist of a permeation cell with
precise temperature gradient control across thin membrane samples, coupled with a mass spectrometer for highly sensitive H flux measurements. A detailed digital twin of the experiment will be constructed to allow rapid inversion of experimental measurements into Q* data.

The new heat-of-transport data will allow the student to test a physically-based model we recently proposed to capture the temperature dependence of heat-of-transport [6], and to calibrate this model for Zr. A key challenge will be to develop approaches to capture and explain differences between the model predictions and the experimental measurement, for example due
to the transient interplay between H thermomigration and hydride precipitation at low temperatures, or evolution of the microstructure. A key complication is that our previous results suggest that molecular dynamics simulations are unlikely to accurately capture thermomigration due to the lack of electronic effects. Here, we propose to leverage the use of machine learning descriptors of atomistic configurations as a proxy to predict evolution in heat-of-transport (similar to an approach we developed to predict thermal conductivity from MD simulations of
defective metals).

To maximise impact, the “thermomigration” digital material twin developed in this project will be integrated into the comprehensive material model being continuously developed by Rolls-Royce for fuel cladding design and optimisation. The combination of sophisticated newexperiments with cutting-edge material simulation and surrogate modelling to tackle a complexmaterials challenge with direct industrial impact strongly reflects the core motivation of the Materials 4.0 CDT.