Mechanical Engineering

The composites industry is under increasing pressure to transition towards a truly circular economy. As growing demand continues to widen the supply gap, we must recover untapped value that would otherwise be lost to landfilling and incineration, which are resource-intensive and environmentally damaging end-of-life pathways. Where recycled fibres are used, they are often downcycled as fillers and low-value reinforcements in their short and randomly aligned form. A key challenge to the effective reintegration of recycled carbon and glass fibres into high-performance products lies in achieving scalable and energy-efficient fibre alignment from irregular, reclaimed feedstocks. Fibre surface attributes and suspension behaviour in alignment systems play vital roles in determining the alignment efficiency, process stability, and the downstream consolidation and performance of remanufactured composites. 

This fully-funded PhD project fits within a wider research programme with industrial partners and an interdisciplinary team working on the development of cross-platform alignment technologies that integrate material science, process engineering and sustainability analysis to deliver scalable solutions for circular composites manufacturing. The successful candidate will contribute to this broader vision by investigating the surface characteristics and suspension dynamics of recycled short fibres used in alignment processes. 

Collaborative links with Gen 2 Carbon, Sigmatex and Teijin Europe in the parent project provide exciting opportunities for knowledge-exchange activities and technical site visits throughout the project. 

Project Objectives

1. Characterise the surface properties of reclaimed carbon and glass fibres from different sources and with varying processing histories.
2. Investigate suspension behaviour, including fibre dispersion, settling and agglomeration tendencies under varying conditions.
3. Study the influence of suspension properties on alignment efficiency, consolidation behaviour, and interfacial compatibility with traditional composite matrices.
4. Explore complementary computational fluid dynamics-discrete element method (CFD-DEM) simulations as a tool to predict fibre-fluid interactions and inform experimental design.

Early application is advised as the position will be filled once a suitable candidate is identified.

Training

As a PhD student, you will take part in a wide range of research activities, including collaboration with international researchers and participation in conferences, workshops and seminars. You will work closely with fellow researchers within the Institute for Materials and Processes (IMP) at the University of Edinburgh’s School of Engineering. Regular meetings and collaborative interactions across the group will provide valuable opportunities for technical exchange and peer learning. 

You will have access to tailored professional development opportunities through the Institute for Academic Development, and technical training will be provided as needed to support your experimental and analytical work. Close alignment with the parent project and its industry partners will facilitate site visits and knowledge exchange activities, enhancing the real-world relevance of your research. 

Note that only applications received via the University’s online system will be considered. All applications should include the following documents: 

• 2–3-page research proposal
• 1-page motivation letter/personal statement
• Curriculum vitae
• Degree transcripts/certificates

•   For any enquiries, please contact: Dr Winifred Obande (w.obande@ed.ac.uk)

•   The University of Edinburgh is committed to equality of opportunity for all its staff and students and promotes a culture of inclusivity. Details: https://www.ed.ac.uk/equality-diversity

•   Supervisor home page https://eng.ed.ac.uk/about/people/dr-wini-obande

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.

We welcome applications from motivated, curious, and technically capable individuals with a first-class or upper 2:1 UK Honours degree (or international equivalent) in one of the following disciplines:

• Materials Science
• Mechanical, Chemical or Manufacturing Engineering
• Applied Physics or Physical Chemistry (especially surface, fluid, or particle systems)
• Other closely related disciplines with a strong experimental and analytical focus

Other Essential Requirements:

• 3D CAD proficiency, ideally using Solid Edge, Creo, or SolidWorks.
• Demonstrable experimental laboratory competence and analytical skills.
• University of Edinburgh English-language entry requirements apply.

Desirable Requirements:

• Experience in wet labs, polymer processing and experimental characterisation.
• Familiarity with surface analysis techniques.
• Some knowledge of CFD, DEM, or multiphase flow modelling.
• Some coding experience, ideally in Python or MATLAB.

Further information and other funding options.

Tuition fees and stipend are available for Home and International students. 

Applications are also welcome from self funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.

Existing buildings entail a third of the energy consumption in the world and contribute a similar share of greenhouse gas emissions. At the same time, they have been shown to have a large potential for improvement in both fronts. Just enhancing operations of what is already in place is estimated to reduce energy and operational emissions by 20% on average.

A significant area of work has focused on whole-building analysis and simulation to rigorously understand performance of existing assets. Here, building audits are combined with holistic physics-based models to improve understanding of what is built and to devise ways to improve operations and retrofit packages. However, such an approach requires specialist knowledge and can easily become onerous given time, interrelationships of physical processes, and resources needed. In addition, it does not link well with metering and building management systems buildings have in place as they only monitor buildings partially.

This project seeks to investigate the role of progressive energy modelling to assist facility managers understand and improve the performance of existing assets. Building systems can be modelled in a wide range of ways, and it is possible to isolate components for dedicated analyses. Then, they can be extended to include further parts relevant to the system. As an example in HVAC systems, heating/cooling generation via heat pumps or boilers could be analysed with a dedicated model, aided by monitoring points typically implemented, like supply/return temperatures or partial loads. Once it is verified that the system works as expected, the model can be extended to represent further parts of the whole system, like losses in distribution systems, emitters, or links with ventilation systems.

Compared to whole-building simulation, this is a bottom-up modelling approach that conceives buildings as a jigsaw of systems that have a closer correspondence with established monitoring practices in facility management. It simplifies complexity, offers faster feedback cycles, retains meaning to explore alternative ways of operating buildings, and could be done in a way that paves the way to whole-building modelling as required.

We are seeking applicants wishing to advance the state of the art in this area, including exploring the role of model complexity, model interoperability, and influence on facility managers' decision-making processes.

This is a joint PhD project between The University of Edinburgh (Dr Daniel Fosas) and Heriot-Watt University (Professor David Jenkins). The context of the work will be the campuses of these institutions and students will have access to their combined training opportunities.

We welcome applications from all qualified candidates, and we wish to particularly encourage applications from groups underrepresented at this level.

To apply to this opportunity, you will need to:

1. Meet entry requirements - note this mainly relates to (a) have a degree classification of at least 2:1 or equivalent, (b) have funding, (c) meet English requirements.
2. Prepare documentation required for conditional admission in the PhD programme - please note that this requires a formal 2-page research proposal (how to write a proposal).

We are available to discuss and give feedback to applicants that meet entry requirements.

A comprehensive training programme will be provided comprising both specialist scientific training and generic transferable and professional skills. The PhD candidate will be introduced to comprehensive training options, within The University of Edinburgh and Heriot-Watt University.

The candidate will have the opportunity to become a teaching assistant following formal training, as well as opportunities to contribute to wider training and outreach activities. Further training in both academic and interdisciplinary skills will be available as part of Edinburgh’s Institute for Academic Development.

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. 

A background related to building performance simulation and building services engineering would be an advantage for the project. The project will require development of building simulation models, off-the-shelve and bespoke ones, and data mining: familiarity or willingness to learn programming languages like Python, Julia, or R will be essential

Please click on this link for 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.

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.