911½ñÈÕºÚÁÏ

This studentship is open to candidates eligible for Home fees only, as defined by . 

Campus: White City

Funding Details:

• Coverage: Home tuition fees, stipend and consumables (£1,000 per year)
• Duration: 36 months
• Study mode: Full-time
• Annual stipend: 
• Supervisor(s): Reshma R Rao, Ifan E.L Stephens and Mary Ryan

We invite applications for a fully funded PhD studentship in the Department of Materials at 911½ñÈÕºÚÁÏ.

The successful candidate will join a dynamic and inclusive team committed to world-class research and academic excellence.

Project description:

This PhD project addresses the urgent need to mitigate rising CO₂ levels by developing sustainable electrocatalysts, based on recycled materials, for electrochemical CO₂ reduction (COâ‚‚RR). This approach enables the conversion of CO₂ into value-added fuels and chemicals using renewable electricity, offering a pathway toward carbon-neutral energy systems. The project focuses on understanding how nanoscale structure, composition, and heterogeneity influence catalytic activity and selectivity, particularly toward multi-carbon products. These challenges are significantly exacerbated when using recycled and repurposed materials as catalyst precursors, since their composition can vary significantly from high-purity virgin materials. This work will investigate the atomistic processes underlying catalyst evolution under reaction conditions, including phase transformations and the role of impurities. Advanced characterisation techniques, such as high-resolution electron microscopy and atom probe tomography (in collaboration with project partners at Centre national de la recherche scientifique (CNRS) France), will be combined with electrochemical testing, X-ray and surface enhanced infrared absorption spectroscopy to establish robust structure–property relationships.

Applicants should have a Master’s degree or (equivalent) with First Class or Upper Second Class in Materials Science, Chemical Engineering, Physics or Chemistry. For information on how to apply, go to:  Application process | Study | 911½ñÈÕºÚÁÏ.

You will be required to submit:

• Personal statement
• CV
• The contact details of two referees – please note that the prospective supervisor cannot be a referee

Please get in touch with Dr Annalisa Neri for further information on the application process.

For further information or informal discussions about the position, please contact:
Reshma R Rao, Assistant Professor.

Closing date: open until filled

Our values are at the root of everything we do, and everyone in our community is expected to demonstrate 911½ñÈÕºÚÁÏ:

• Respect
• Collaboration
• Excellence
• Integrity
• Innovation

Students are also required to comply with all 911½ñÈÕºÚÁÏ policies and regulations

We are committed to equality of opportunity, to eliminating discrimination and to creating an inclusive working environment for all. We encourage candidates to apply irrespective of age, disability, marriage or civil partnership status, pregnancy or maternity, race, religion and belief, gender reassignment, sex, or sexual orientation. You can read more about our commitment on our webpages

This studentship is open to candidates eligible for Home fees only, as defined by .

Campus: South Kensington and Rutherford Appleton Lab (Harwell, Oxford)

Funding Details: 

• Coverage: Home tuition fees, stipend, and up to £2,000 per year will be available for travel and subsistence to support travel to STFC sites and appropriate conferences and workshops.
• Duration: 42 months
• Study mode: Full-time
• Annual stipend: 

Supervisor(s): Sean Collins (911½ñÈÕºÚÁÏ), Mohsen Danaie (Diamond Light Source) and Jaehoon Cha (STFC).

Project Description: 

Computer-driven collection of scientific data has allowed datasets to be acquired that are so large it would be impossible for a human being to analyse them. Making use of these vast quantities of data will allow for a step change in the science of new materials known as halide perovskites, a promising building block for solar cells and energy-efficient displays and lighting. As part of the Ada Lovelace Centre PhD studentship programme, this project is a joint PhD project across the Science Technology and Facilities Council (STFC), Diamond Light Source, and 911½ñÈÕºÚÁÏ. The project will advance artificial intelligence tools to pull out the important scientific details from big datasets covering the chemical composition and structure of materials spanning the scale of a few atoms all the way up to the size of a working device. Learning the physics and chemistry behind the data with artificial intelligence, often otherwise used as a ‘black box’ analysis, requires substantial new development of software and ways of bringing data and scientific interpretation together for new materials science discoveries for tomorrow’s energy needs.

More information:

 Halide perovskites are among a select group of materials defining next-generation semiconductor technologies from photovoltaics for grid-scale energy generation [1] to high-efficiency light emitting diodes [2] and photodetectors for sensors. Despite their transformative potential, limitations on the performance and lifespan of halide perovskite devices originate in disorder (e.g. octahedral tilting [3]) and heterogeneity [4] spanning from the nanometre scale of grain boundaries and crystallographic defects to the centimetre scale of devices. Recent progress in the science of halide perovskites has emerged from spatially resolved nanobeam scanning electron diffraction (SED) and correlated elemental mapping in the STEM. Yet data is acquired under ‘low-dose’ conditions resulting in signals that are noisy—extremely so for weak, simultaneous spectroscopy signals in X-ray energy dispersive spectroscopy (EDS). The imperative with such data is to find low-concentration features within the noise, prompting much larger datasets under automatic control of the stage and microscope, as recently demonstrated at the electron Physical Sciences Imaging Centre (Diamond Light Source) [5].

AI-automated workflows – for both data reduction and, critically, for data interpretation – are poised to realise these gains. This project will be centred on developing autoencoder (AE) workflows [6] (e.g. disentangling, variational AEs), a set of AI neural network structures that make use of two halves: The first half encodes the data to a latent space and the second decodes the data from that space. AEs are powerful as classifiers, but the latent space representation can also be used to ascertain physical parameters, going beyond a ‘black box’ AI classifier. This process requires not a single AE but a well-designed architecture tailored to the types of inputs and outputs sought.

The PhD researcher is expected to spend 50% of the time at Rutherford Appleton Lab (Harwell Oxford) and 50% of the time in the Department of Materials, 911½ñÈÕºÚÁÏ (South Kensington). The PhD researcher will also participate in 911½ñÈÕºÚÁÏ-based cohort activities for PhD researchers throughout the degree.

[1]        J.C. Blakesley et al. J. Phys. Energy 6 (2024) 041501.

[2]        Z. Chen et al. J. Phys. Photonics 6 (2024) 032501.

[3]        T.A.S. Doherty et al. Science 374 (2021) 1598–1605.

[4]        K. Frohna et al. Nat. Nanotechnol. 17 (2022) 190–196.

[5]        J. Ryu et al. ScienceOpen Research, 2025: p. e197. https://doi.org/10.14293/APMC13-2025-0197.

[6]        J. Cha et al. Nat. Mach. Intell. 7 (2025) 307–314.

This studentship is funded by the Centre for and the Department of Materials. It is open to candidates eligible for Home fees only, as defined by . 

Campus: South Kensington

Funding Details:

• Coverage: Home tuition fees, stipend and consumables (£1,000 for the first 3 years)
• Duration: 48 months
• Study mode: Full-time
• Annual stipend: 
• Supervisor: Florian Bouville

Deadline: 14 May 2026

A number of key technologies are locked behind ceramic materials innovation. Nuclear fusion require plasma facing shields that needs to be strong and tough, but also temperature resistant and good neutron attenuator, while solid state battery electrolytes must be fast ionic conductors and resistant to crack induced by lithium dendrite. While we are actively searching for compositions that present all these requirements, we can also leverage a material’s microstructure to solve some of them. Ceramic composites are now being developed for these applications and more. We have at our disposal architectures based on reinforcements such as long-fibres and particulates, and their fabrication relies on first producing the reinforcements and then surrounding/mixing them with a different material. Other architectures, based on metamaterials or natural materials design, rely on more complex and regular architectures that cannot be achieved by conventional methods. These designs hold the key to combination of toughness, stiffness and strength beyond the more established composite microstructures, for instance strut/shell-based architecture for lightweight and strong components, or interlocking geometry for their resistance to fracture and impact. In theory, composites with interlocking elements can be used to add a strain hardening effect in brittle materials, reaching toughness and strain at failure beyond any other concepts. They will remain theoretical until we can develop a process capable of making these with a high spatial resolution. Indeed, ceramic strengths are highly size-dependent so making strong composites demand controlling their microstructure and composition at the smallest scale to exploit this effect.

Our fabrication design space remains limited and so is our exploration of microstructure-properties relationships, a limitation multi-material 3D printing can start solving. Digital Light Processing (DLP) specifically controls the curing of light-sensitive inks with a spatial accuracy in the tens of micron range within centimetre-sized 3D shapes for the best printer. Adding the capacity to have multiple inks unlock the possibility to make and study rapidly a series of composite architectures, with different level of regularity and 3D complexity.

Our group is currently building a DLP printer, using a UV-projector reaching 18µm/pixel and two robotic platforms capable of linear movement micron-level accuracy, one platform being responsible for switching between inks. These capabilities, once improved in the first part of this project, will allow us to explore microstructure/properties relationship so far out of reach. We will start with simple brick-and-mortar architectures and expand to more complex design with elements that can interlock during fracture or impact.

The goal of this project will thus be first to: (i) to develop the printer capabilities further, (ii) to use colloidal processing to develop ceramic inks that can be printed and (iii) explore the microstructure to strength/toughness relationships in interlocking composites microstructures.

The first part will be to add a sensor to the printing platform to measure the peeling forces and add a feedback loop to the printing program to limit them, as well as adding a cleaning station to avoid the mixing of the inks during printing. The second goal will be centred on using model ceramic inks that allows for an easy final processing (debinding and co-sintering) and visualisation of the microstructure after printing. The final goal will be to design composites microstructure and study their toughness using mechanical testing, comparing them with more traditional composite microstructures. The fidelity with which we can design these microstructures will be monitored and we will use different minimisation algorithms to adapt iteratively the printing and converge to the desired microstructure, with the possibility of adding an image-based machine learning model to learn from these results and expand to other design/inks.

This new manufacturing method will not be limited to structural materials and the designs targeted by this project but will instead be used in the future to help fabricate and explore rapidly a large design space of complex architectures for multiple applications.

For general enquiries, please contact doctoral-training@royce.ac.uk.

For application-related queries, please contact Dr Annalisa Neri (a.neri14@imperial.ac.uk). Please note that each partner of the CDT in Materials 4.0 will have its own application process.

If you have specific technical or scientific queries about this PhD, we encourage you to contact the lead supervisor, Prof. Florian Bouville (f.bouville@imperial.ac.uk).

Campus: South Kensington

Funding Details: 

• Coverage: Home or overseas tuition fees, a stipend, and up to £1,000 per year for travel and consumables.
• Duration: 42 months
• Study mode: Full-time
• Annual stipend: 

Supervisor(s): Ann Huang (911½ñÈÕºÚÁÏ)

Project Description: 

Next-generation batteries such as solid-state batteries (SSBs) have great potential to improve the safety and energy storage performance of current lithium-ion batteries (LIBs). However, slow ion diffusion in SSBs currently restricts their performance. This research will focus on two areas relevant to electric automotive applications: (i) development of new electrode and solid-state electrolyte materials for SSBs, and (ii) fabrication of electrodes into batteries using state-of-the-art processing, characterisation and performance testing facilities. The project aims to build a fundamental understanding in chemistry and materials science to produce SSBs with performance surpassing current LIBs, combining processing techniques, characterisation methods and modelling.

For application-related queries, please get in touch with Annalisa Neri.

If you have specific technical or scientific queries about this PhD, we encourage you to contact the lead supervisor, Ann Huang.

This studentship is funded by the Centre for and the Department of Materials. It is open to candidates eligible for Home fees only, as defined by . 

Campus: South Kensington

Funding Details:

• Coverage: Home tuition fees, stipend and consumables (£1,000 for the first 3 years)
• Duration: 48 months
• Study mode: Full-time
• Annual stipend: 
• Main Supervisor: Dr Abigail Ackerman

This PhD project will focus on the upscaling of low-temperature electrolytic reduction of iron, a process that is currently confined to laboratory research. To achieve full industrial implementation, digital twins will be essential, enabling remote sensing of electrolysis cells to improve efficiency and allow continuous system monitoring. The project will involve developing a lab-scale system equipped with advanced monitoring tools that closely replicate industrial conditions. Alongside this, a digital twin will be created to optimise key parameters—such as energy supply and alkalinity—providing a scalable framework for transitioning the technology to industrial application.

Low temperature electrolysis is an emerging technology that has the potential to revolutionise the ironmaking industry. Iron and steel production currently accounts for around 8% of global COâ‚‚ emissions, with approximately 70% of these emissions resulting from the reduction of iron ore (naturally found as Feâ‚‚O₃). The dominant reduction method—carbon-based blast furnace processing at ~2100 °C—releases oxygen from the ore, which bonds with carbon to form COâ‚‚. On average, producing one tonne of iron this way emits about 1.8 tonnes of COâ‚‚.

Low-temperature electrolysis offers a promising alternative. Over the past decade, this experimental method has shown potential to significantly lower both carbon emissions and energy demand compared with other green technologies such as Direct Reduced Iron (DRI), which uses hydrogen as a reducing agent. Unlike many existing approaches, this method produces only oxygen as a by-product and does not require high-purity ore. This makes it particularly suitable for processing lower-grade iron ores and recovering iron from waste streams such as mine water and tailings.

An important yet underexplored aspect is the application of digital twins to remote sensing of electrolyser cells. In multi-electrolyser systems, digital twins are critical for balancing power input across cells and for predicting component degradation under demanding operating conditions. This project will build on existing digital twin blueprints for electrolysers by developing a robust sensor system that feeds real-time data into a digital twin, designed and implemented by the student. This capability will be central to ongoing upscaling efforts within both the supervisor’s research group and the project’s industrial sponsor.

The aims of this project are:

1. Design and build a lab-scale electrolyser system with real-time monitoring capabilities.
2. Develop and validate a digital twin model using remote sensing data.
3. Optimise key parameters such as energy efficiency and electrolyte alkalinity.
4. Demonstrate scalability by comparing lab-scale results with industrial requirements.
5. Use these findings to widen the scientific understanding of low temperature electrolytic reduction of iron, including electrode degradation and iron quality

Apply by 06/02/2026

For general enquiries, please contact doctoral-training@royce.ac.uk.

For application-related queries, please contact Dr Annalisa Neri (a.neri14@imperial.ac.uk).

If you have specific technical or scientific queries about this PhD, we encourage you to contact the lead supervisor, Dr. Abigail Ackerman (a.ackerman14@imperial.ac.uk).

This project is suitable for students with a background in materials science, metallurgy and chemical engineering.