Materials and Processes
Advanced electronic/optoelectronic technologies designed to allow stable, intimate integration with living organisms will accelerate progress in biomedical research; they will also serve as the foundations for new approaches in monitoring and treating diseases.
Lightweight composites are growing rapidly across industries due to a powerful combination of performance benefits, economic incentives, and environmental pressures. Among these, thermoplastic composites are experiencing particularly rapid growth because of their recyclability, which distinguishes them from traditional non-recyclable thermoset composites. Thermoplastics can be reheated and reshaped multiple times, making them recyclable — unlike thermosets, which are permanently set after curing. This characteristic aligns perfectly with the growing global emphasis on sustainable materials and circular economy principles. As industries face increasing pressure to reduce their carbon footprints, thermoplastic composites offer a viable path to achieving these environmental goals.
In addition to sustainability, thermoplastic composites generally offer superior mechanical properties, such as high toughness and impact resistance, excellent fatigue performance, and high damage tolerance. Components made from thermoplastic composites can be welded or repaired using heat, a distinct advantage over thermosets, which cannot be reshaped or repaired once cured. This enhances both the durability and serviceability of composite structures, making them attractive for a wide range of applications.
However, many high-performance thermoplastic composites require very high melting temperatures—often in the range of 250–400°C—during moulding, consolidation, or welding. This makes the processing energy-intensive, especially at large industrial scales. The equipment needed for such processing must generate (and thus withstand) high pressures and temperatures, which increases capital costs, demands more energy to run, and adds complexity to maintenance and safety protocols. In many industries, these higher energy demands currently outweigh the benefits of recyclability, particularly when production volumes are very high or when large structures are to be manufactured.
To overcome these challenges, there is a critical need to use low-melt thermoplastic resins for composites that can be in-situ polymerised in an energy-efficient way. Hence, innovative processing methods must be explored and optimised to significantly reduce the carbon footprint associated with composites manufacturing. This PhD project will investigate processing of cyclic butylene terephthalate (CBT) composites in an energy-efficient way.
The successful applicant will gain hands-on experience with the fundamentals of composites manufacturing, composites characterization and processing techniques as well as with induction heating. S/he will learn to operate instruments such as differential scanning calorimetry (DSC), scanning electron microscopy (SEM), and rheometers, as well as perform thermal and electrical conductivity measurements and mechanical testing. Important part of the project is the development of a novel methodology for processing composites by targeted heating using induction heating. Furthermore, students will be trained in the critical analysis of experimental data, advanced material characterisation, and scientific writing skills, preparing them for impactful careers in composite materials research and industry.
The project is part funded by an industrial collaborator.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Tuition fees + stipend are available for applicants who qualify as Home applicants.
To qualify as a Home student, you must fulfil one of the following criteria:
- You are a UK applicant.
- You are an EU applicant with settled/pre-settled status who also has 3 years residency in the UK/EEA/Gibraltar/Switzerland immediately before the start of your programme.
Applications are also welcomed from those who have secured their own funding through scholarship or similar.
Whether it is the substantial cooling requirements of future data centres or energy-dense batteries for next-generation electric vehicles, the need for energy-efficient electronics cooling systems is ubiquitous. This is because while recent developments have produced ever-smaller and ever-denser devices, heat fluxes comparable to the surface of the Sun can be generated at hot spots, producing high temperatures that adversely impact their performance and raise risk of catastrophic failure. In the last decade and a half, novel 2D nanomaterials have been developed with unique thermal properties (e.g. ultrahigh thermal conductivity). These nanomaterials can be used to form surface coatings to enhance heat transfer from the extremely hot surfaces of electronic devices into the adjacent coolant liquid.
However, our understanding of thermal transport at this nanomaterial/liquid interface is currently limited. For 2D nanocoatings, the nanomaterial can be either carbon-based (graphene nanoparticles or nanoflakes, nanopores, graphene oxide nanosheets etc), boron-based (boron nitride nanosheets, nanotubes, etc) or hybrid (e.g. boron carbon nitride). Similarly, while water is the most studied coolant liquid, realistic applications involve dielectric fluids (e.g. benzene, pentane). Molecular dynamics (MD) simulations represent a powerful tool to study such interfaces, but MD of nanomaterial/liquid interfaces require well-calibrated intermolecular potentials, which don’t currently exist. This project will rely on recent advances in neural networks to develop machine learning potentials (MLPs) for MD simulations of realistic nanomaterial/coolant-liquids and use these to gain fundamental insights into interfacial thermal transport. The goals are to:
1) run ab-initio molecular simulations to sample relevant nanomaterial/liquid interfaces.
2) construct new MLPs by using generated data from 1) and validate them.
3) use MLPs to run classical MD simulations and characterise thermal transport.
This PhD project will be based within the School of Engineering, University of Edinburgh. This PhD project will be supervised by Dr Rohit Pillai and Dr Eleonora Ricci, and the successful applicant will join an active, friendly, and collaborative research group (see https://multiscaleflowx.github.io/). Our group makes extensive use of ARCHER2 – the UK’s national supercomputer, which is based in Edinburgh. This PhD will give the successful applicant the skills and experience to become a future leader in either academia or industry. The supervisors will provide the successful applicant with exceptional research and training opportunities, including:
• regular weekly meetings to discuss the research progress.
• opportunities for travel to participate in workshops/summer schools dedicated to advanced computational methods, as well as present results in international conferences.
• training and experience in state-of-the-art engineering research.
• mentoring from other investigators and experienced postdoctoral researchers.
• exceptional career development opportunities with strong institutional support of early career researchers.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Tuition fees + stipend are available for Home/EU and International students
Asphalt recycling gained prominence since the 1970s, partly initiated by the oil crisis influencing the availability of bitumen as binder material. Since then, recycled and reclaimed asphalt continued to be part of the mix used in road and pavements providing a cost effective and environmentally friendly option with a potential to decarbonising the industry. Recent examples of roads using recycled asphalt include: 50% recycled asphalt was used in paving a section of M25 between junctions 25 and 26 and a section of A388 Bournemouth Spur Road, Dorset was paved using all the old road materials.
Rolled asphalt pavement comprises of different courses primarily the wearing, binder, base, subbase and capping layer. The degree of compaction determines the stiffness and strength of material along with its resistance to deformation and durability of the mixture. Compaction of the asphalt along with the binder results from the operation of the paving/construction equipment to impart systematic static, shearing and vibrational loads to achieve the required properties of each of the aforementioned course. The pavement is expected to withstand the design traffic load.
In the drive towards net zero carbon emission, there is an urgent need to significantly increase the use of the 100% recyclable Recycled Asphalt Pavement (RAP) in pavement construction. This poses significant challenges in the design and optimisation of the production and construction processes for which this current project seeks to address. For instance, is it possible to better characterise the RAP in terms of material properties to provide a more accurate initial assessment of its recycling readiness? Is it possible to match to assess, based on the RAP's material characteristics and the prevailing loading regimes, whether it would meet the required highway standards?
The aim of the project is to develop a deeper understanding of the RAP pavement construction and establish an experimentally calibrated numerical model to predict the compaction mechanics of recycled asphalt pavements during construction as well as operational period. The model will integrate the mechanics at different length scales. Experimental programme will include time-resolved (4D) X-ray tomography to capture the micromechanics of the granular assembly.
This PhD project is advertised as a part of the Edinburgh Research Partnership in Engineering, a joint partnership between the University of Edinburgh and Heriot-Watt University. The successful candidate will be supervised by a team consisting of academics from the University of Edinburgh and Heriot Watt University (HWU). The Heriot-Watt University supervisor for this project will be Dr Elma Charalampidou. Some of the experiments involving micro x-ray CT system will be undertaken at HWU.
The selection process is in two phases:
Stage 1: Interested candidates should contact Dr Amer Syed at Amer.Syed@ed.ac.uk by 7 February 2025 with their CV and a covering email. Potential candidates will be invited to an interview. Selected candidate will progress to Stage 2.
Stage 2: Selected candidate will complete a formal application to the University of Edinburgh by 12 February 2025. This application will be assessed by a panel for funding. Please note that this studentship attracts enhanced stipend, while the exact details yet to be finalised, for 2024, it was £21,400 per annum.
Home and overseas students are encouraged to apply.
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Tuition fees + stipend are available for Home/EU and International students.
From sandcastles to powder metallurgy, granular materials are ubiquitous in engineering and natural environment. Understanding their behaviour under a range of loading conditions is essential in ensuring the structural integrity of the granular system e.g., landslides, chemical/pharmaceutical applications such as compacted tablets, food processing etc.
The mechanical response of a granular assembly depends on the interaction of the individual grains. In most of the natural and engineering systems, this interaction is further complicated by the presence of fluids and temperature gradient resulting in convective mass transport. The thermomechanical behaviour of the granular assembly depends on the temperature/concentration gradient, viscosity of the fluid, variation in fluid saturation, compressibility of the fluid etc. The presence of fluid would also influence the relative motion of the particles, especially in case of particles with varying size and shapes, and directly contribute to the nature of compaction and flow of the granular assembly.
The aim of the project is to develop a deeper understanding of the mechanics of granular assemblies subjected to convective mass transport and to formulate a multiscale multiphysics model to predict the thermomechanical behaviour of granular assemblies. The model will be developed and calibrated using high quality experimental data acquired at multiple length scales. Custom designed experiments will be conducted in an x-ray CT environment to study the micromechanics of the underlying processes using time resolved x-ray tomography (in 4D).
There are four application areas for this project and the successful candidate would be able to select one of these areas.
Geological/geophysical application: Geothermal systems, particularly Enhanced Geothermal Systems where the energy from underground hot rock/fractured rock is used to generate electricity.
Steel production: Porous coke in the granular assembly of the blast furnace charge provides energy, heat and gas required to reduce the iron ore. Improved design of the granular assembly has potential to minimise the CO2 emission in the steel making process.
Recycled Asphalt Pavements: Reclaimed and recycled asphalt are used in road pavements providing a cost effective and environmentally friendly option with a potential in decarbonising the industry.
Powder bed fusion, a metal additive manufacturing technique: The nature of granular assembly of metal powder bed informs the quality of the finished product.
This PhD project is advertised as a part of the Edinburgh Research Partnership in Engineering, a joint partnership between the University of Edinburgh and Heriot-Watt University. The successful candidate will be supervised by a team consisting of academics from the University of Edinburgh and Heriot Watt University (HWU). The Heriot-Watt University supervisor for this project will be Dr Elma Charalampidou. Some of the experiments involving micro x-ray CT system will be undertaken at HWU.
The selection process is in two phases:
Stage 1: Interested candidates should contact Dr Amer Syed at Amer.Syed@ed.ac.uk by 7 February 2025 with their CV and a covering email. Potential candidates will be invited to an interview. Selected candidate will progress to Stage 2.
Stage 2: Selected candidate will complete a formal application to the University of Edinburgh by 12 February 2025. This application will be assessed by a panel for funding. Please note that this studentship attracts enhanced stipend, while the exact details are yet to be finalised, for 2024, it was £21,400 per annum.
Home and overseas students are encouraged to apply.
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Tuition fees + stipend are available for Home/EU and International students.
The importance of clustering, or microphase separation, is increasingly recognized, with applications in many technologies including nanomaterials and pharmaceutical crystallization. It is also important in nature; for example the membraneless organelles within biological cells. However, the mechanisms leading to such clusters are not completely understood.
This project aims to improve understanding of microphase separation in complex coacervates. These particular clusters, or microphases, are formed by, for example, mixtures of oppositely charged polyelectrolytes and might also describe some membraneless organelles.
To this end, the successful candidate will develop thermodynamic models of equilibrium clustering in binary mixtures with competing short-range and long-range interactions. The aim is to model and understand the link between particle interactions and microphase separation in complex coacervates.
It is expected that the applicant will have a good degree in Engineering, Physics, Chemistry, Mathematics, or any other related subject. We are particularly keen to hear from applicants who want to develop expertise in molecular theories of fluids. Prior experience in this area is useful but not a requirement.
The successful student, depending on eligibility, will have opportunities for teaching and further training with in the university, as well as participation in the intellectual community provided by the School of Engineering’s Institute for Materials and Processes, in which they will be based.
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity: https://www.ed.ac.uk/equality-diversity
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in Engineering, Physics, Chemistry, Mathematics, or any other related subject possibly supported by an MSc Degree. Prior experience in molecular theories of fluids is useful but not a requirement.
Further information on English language requirements for EU/Overseas applicants.
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.
The advent of Industry 4.0 has ushered in a new era of manufacturing, marked by the integration of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and digital twins. This research project will investigate the methodologies and technologies necessary to create an accurate and dynamic digital twin that reflects the real-time status of a manufacturing process, thereby enabling enhanced decision-making, predictive maintenance, and improved production efficiency.
This project will be jointly supervised by:
Prof Prof Jonathan Corney j.r.corney@ed.ac.uk
Dr Matjaz Vidmar matjaz.vidmar@ed.ac.uk
Smart Products Made Smarter
The PhD project forms part of a larger Prosperity Partnership Programme, Smart Products Made Smarter, a collaboration with Heriot-Watt University, University of Edinburgh and Leonardo.
We are pleased to invite applications for a PhD studentship to work as part of a leading team of experts. This studentship will be supported by an enhanced stipend of £20,716 per year over 3.5 years.
This grant, sponsored by the EPSRC, is a collaboration between academia and Leonardo. There are currently PhD opportunities available to work on diverse topics as part of this collaborative team. The work will involve strong links with industry.
The research addresses a broad range of challenges. These challenges exemplify future product lifecycle management from smart concept, design, development and manufacture to enhanced end-user capability, united by a common digital thread to enable smarter products to be made smarter. Each challenge area has clearly identified initial research themes and associated research challenges to be addressed and these are indicated below:
Challenge 1 (C1) the Making challenge: To create new hybrid manufacturing processes, that combine multiple Additive Manufacturing (AM) process with precision machining and coating processes to create components that disrupt the traditional functional trade-offs of Size, Weight and Power (SWaP) through techniques such as varying the material properties within a part and harnessing the digital production of optical components.
Challenge 2 (C2) the Manipulation challenge: To create new handling processes that fully exploit the digital data flows which define custom components whose shape and functionality is tailored to production by dexterous, highly adaptable robots that are programmed dynamically.
Challenge 3 (C3) the Computation challenge: To create new signal processing & machine learning methodologies that enable intelligent, digital & connected sensor products while mitigating the data deluge from the multiple sensors produced by Leonardo operating across the EM spectrum.
The themes represent areas that could form the basis of your PhD. These PhD positions offer great flexibility and we welcome the opportunity to explore other ideas & themes.
Please note that this advert will close as soon as a suitable candidate is found.
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity: https://www.ed.ac.uk/equality-diversity
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Please note that as this is a defence based project, only UK/EU students are eligible to apply. International applicants are not eligible.
Tuition fees + stipend are available for applicants who qualify as:
- a UK applicantan EU applicant (International/non EU students are not eligible)
Funding is available through EPSRC Prosperity Partnership Programme. As this is a defence related project there are nationality restrictions (see above).