Mechanical Engineering

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.

Further information and other funding options.

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

Further information and other funding options.

This PhD project aims to design heat integration strategies within multi-vector energy systems to enhance overall system flexibility and efficiency.

The route to net zero faces two main challenges: first, the increasing integration of non-dispatchable and variable renewable energy resources, such as wind and solar power, creates significant challenges for energy systems, notably in terms of maintaining reliability and balancing supply with demand; and, second, there is almost no progress and not even a credible roadmap for heat decarbonisation (low temperature space heating as well as high temperature industrial heat). By focusing on the thermal aspects of energy systems, and particularly on strategies for efficient heat integration, this research aims to provide novel solutions that enhance system stability and provide affordable and sustainable heat.

The project will investigate heat integration techniques across various levels of the energy system, including industrial processes, district heating networks, and residential heating solutions. Key areas of focus will include the integration of advanced thermal storage technologies, the utilisation of waste heat recovery, and the implementation of innovative heat pump technologies. This multi-scale approach ensures that the project addresses both high-grade industrial heat and low-grade residential heat requirements.

A significant component of the research will involve the development of mathematical models and simulation tools to evaluate potential heat integration scenarios. The models and tools will be built on existing open-source tools in the Institute for Energy Systems, commercials tools such as TRNSYS and open-source tools such as PyPSA. These tools will help in identifying optimal ways to deploy thermal energy storage and recovery, thus enabling better management of renewable generation variability. The methodologies developed will consider not only energy efficiency but also economic and environmental impacts, ensuring that the solutions are sustainable both technically and financially.

The candidate will develop a wide range of skills in simulation, optimisation, and data analysis which are widely applicable to future career development. Additionally, there are opportunities for engaging with an open and inclusive community of open-source energy system developers both within IES and globally. 

Overall, this PhD project offers a comprehensive approach to enhancing system flexibility through heat integration, addressing critical challenges in the transition to a more sustainable and reliable energy future.

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.

Essential background: 

• 2.1 or above (or equivalent) in Engineering, Mathematics, Physics, Energy Engineering/Economics, Informatics, or similar
• Programming in Python, Julia or other high-level language

Desirable background:

• Energy system modelling and optimisation
• Data analysis, optimisation and/or machine learning
• Experience in thermal energy system modelling

Applications are welcomed from self-funded students, or students who are applying for  scholarships from the University of Edinburgh or elsewhere

Further information and other funding options.

This PhD project delves into the dynamics of residential energy consumption, system flexibility, and employs the systems transition engineering processes (STEPs) to tackle energy poverty with novel utility network-to-end-use flexibility opportunities. The research is framed around the critical need to create resilient urban energy systems that not only adapt to fast-paced technological and environmental changes but also promote energy equity and efficiency.

In urban environments, residential areas are key consumers of energy and greatly influence the overall dynamics of urban energy flow. The primary aim of this research is to innovate, model and optimise the intake and distribution of energy in residential sectors and examine how these modifications can alleviate energy poverty, characterised by lack of access to reliable and affordable energy services. This involves understanding the specific energy needs of underserved populations and integrating solutions that ensure equitable energy distribution.

Transition engineering principles guide the project's approach, integrating systems thinking, predictive modelling, and simulation techniques to explore novel and practical engineering adjustments for improving system flexibility and reliability amid increasing green energy integration and fluctuating demand. Expertise will be gained in grid and network technology and commercial operations, and energy end uses—from heating and lighting to appliances and electronic devices. The project will assess initiatives like participatory demand-response technologies, energy-efficient retrofitting, integrated storage, and community energy systems.

Moving beyond technical analysis, the study will incorporate socioeconomic data to paint a more accurate picture of energy consumption patterns and barriers to energy access in various residential demographics. Simulation tools will evaluate how different interventions might impact energy affordability and reliability at the household level and their wider effects on the energy system's flexibility and sustainability.

Policy implications will also be a significant focus of this research. By identifying regulatory and institutional barriers to equitable energy distribution and system flexibility, the project aims to suggest robust policy measures that can support broad adoption of efficient and equitable energy solutions.

The expected contribution of this PhD project is pioneering energy transition shifts for adaptable, forward-thinking strategies that enhance energy system infrastructure in urban areas, ensuring that they are not only sustainable and flexible but also fair and responsive to the needs of all community members. The PhD candidate will have a Mechanical or Electric Power Engineering qualification, utility industry or energy systems engineering experience, aptitude for modelling, and passion for energy systems transition engineering. Candidates who are systems thinkers are preferred.

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.

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.

Essential background: 

• 2.1 or above (or equivalent) in Engineering, Mathematics, Physics, Energy Engineering/Economics, Informatics, or similar
• Programming in Python, Julia or other high-level language

Desirable background:

• Energy system modelling and optimisation
• Experience in energy systems transition engineering
• Data analysis, optimisation and/or machine learning
• Experience in energy system modelling

Applications are welcomed from self-funded students, or students who are applying for  scholarships from the University of Edinburgh or elsewhere

Further information and other funding options.