Multiscale Thermofluids

Gary is a Reader in Surfaces and Wetting at the School of Engineering at the University of Edinburgh.  He earned a BSc in 2005 and a PhD from Nottingham Trent University in 2009. Prior to joining the University of Edinburgh, he worked in industrial research at the Hewlett-Packard Display Research Lab and later served as an anniversary research fellow at Northumbria University, Newcastle. At Edinburgh, he is involved in and leads experimental research and development within the Wetting, Interfacial Science and Engineering group in the  Institute of Multiscale Thermofluids.

• BSc (Hons) Physics with Astrophysics
• PhD "Voltage Programmable Liquid Optical Interfaces

• Member of Istitute of Physics (IOP)
• Fellowship of the Higher Education Academy
• Member of the EPSRC College of Reviewers
• Committee member if IOP Printing, Graphic and Imaging Group

General Engineering 1: Course Organiser

Chemical Engineering Design 1: Co-Course Organiser

Electrical and Electronic Engineering 1: Co-Course Organiser

Gary's research focuses on the applications of surface coatings, and he designs and builds experiments and instrumentation to produce and understand the adhesion and friction of droplets on surfaces. He has conducted experimental research into surface coatings and their various applications. The coatings he has developed can be used in many applications, including heat and mass transfer and anti-fouling. 

As part of the WISE group, Gary collaborates with theoreticians to develop instrumentation and experiments to test and understand solid-liquid interfaces. His experimental research has led to multiple publications in high-ranking journals, with over 50 peer-reviewed articles in journals such as Langmuir, Nature Communications, Soft Matter, and the Journal of Fluid Mechanics.

We invite applications for a PhD position to advance flue gas cleaning technologies from industrial emissions, particularly in Energy-from-Waste (EfW) power plants. The EfW solutions incorporate advanced flue gas cleaning systems that notably reduce landfill waste, lower emissions, generate energy, and assist in material recovery, thus supporting a sustainable closed-loop circular economy. The appointed researcher will contribute to the EPSRC funded M2CLEAN project, which intends to thoroughly investigate the complex dynamics of particle interactions at various scales, including those between solid particles, liquid droplets, and gas phases. The research work will tackle key operational challenges such as regulating temperature and humidity, alongside optimising particle size and distribution to increase emission removal efficiency. The primary goal of this project is to create experimentally informed predictive models that detail these inter-particle interactions, enhancing understanding and efficiency of semi-dry flue gas cleaning systems.

The current Ph.D. position focuses on the experimental investigation of particle-droplet interactions under acoustic levitation. The project aims to foster a new understanding of the spatiotemporal scales and the controlling parameters of particles when subjected to varying temperature and humidity. The project offers hands-on experience in setting up multiphase experiments and using several state-of-the-art optical diagnostics, such as high-speed shadowgraphy, Particle Image Velocimetry (PIV), and Planar-Induced Fluorescence (PLIF).

The ideal candidate will be interested in fluid dynamics, reacting flows, and laser diagnostics and have programming experience in at least one language (e.g., Python, MATLAB, etc.). The selection process considers the comprehensive strength of the entire application, including the academic qualifications, personal statement, CV, and references. Ideally, candidates should have a strong background in fluid dynamics and the development of experimental methodologies.

The project includes close collaboration with TU Darmstadt, Germany and industrial partner Kanadevia INOVA, Zurich, Switzerland. This collaborative environment will offer the opportunity to learn from and contribute to a diverse team of experts. 

The intended PhD start date is in beginning of April 2026. If a suitable candidate is found, this position may close earlier than the closing date. 

Informal inquiries may be addressed to Dr Khushboo Pandey at kpandey@ed.ac.uk.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline. The candidate should have a master’s degree in either Physics or Engineering.

Further information on English language requirements for EU/Overseas applicants.

Tuition Fees and stipend are avilable for Home/EU and International students.

Further information and other funding options.

Are you eager to push the frontiers of fluid dynamics and AI to tackle real-world challenges in crowd safety, urban planning, and event management? Join our EPSRC-funded project FLOCKS (Fluid dynamics-Like Open-source Crowd Knowledge-driven Simulator) as a PhD student and help shape the future of crowd modelling.

FLOCKS aims to develop the world's first real-time, open-source simulator of large, dense crowd dynamics for both academic and industrial applications. 

Your research will focus on creating a continuum-based fluid dynamics model of human crowds, treating them as "thinking fluids" and using coarse-grained observables, such as density, mean velocity, and stress-like quantities, as descriptors. Drawing on active matter modelling, you will explore ways of incorporating the non-local perception and decision-making of pedestrians into constitutive relationships and boundary conditions with the aim of capturing realistic crowd behaviour. Where possible, you will also explore coupling the crowd model with hazards such as the spread of smoke and fire during incidents, contagion in pandemics, or violence in demonstrations, which will enable a multi-risk approach to assessment and decision support in high-stakes scenarios. 

You will work closely with a postdoctoral researcher to develop complementary data-driven approaches that use machine learning to inform, parameterise and validate crowd dynamics models. This collaboration will establish a continuous feedback loop to refine your model and provide valuable opportunities for knowledge exchange.

You will implement the final model in an open-source simulation environment, and your final demonstrator will simulate iconic Edinburgh events (e.g. Hogmanay on Princes Street, an Edinburgh derby football match, or a Murrayfield Stadium concert) using pre-captured datasets to showcase the predictive capabilities of the simulator.

Thanks to our partnerships with world-leading experts in crowd safety engineering and open-source software development, your work will directly impact public safety, urban planning and event management in the real world.

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

It is intended that the PhD start date will be 1 September 2026, and applicants should select that entry point when applying to the PhD programme.

Minimum entry qualification

• First or Upper Second-Class (2:1) honours degree or equivalent in Engineering, Physics, Applied Mathematics or a clearly related area, with a focus on continuum mechanics, differential equations, and numerical methods or a closely related area.
• Evidence of research in computational engineering, with a specific focus on hydrodynamic modelling of complex systems or a closely related area.
• Proficiency in scientific programming (e.g., Python, Fortran, C++).

Further information on University’s English language requirements for EU/Overseas applicants.

Desirable criteria

• Training in machine learning, ideally applied to model discovery and physics-informed approaches.
• Experience of computational fluid dynamics or agent-based simulation software.

Further information and other funding options.

School of Engineering stipend for 3.5 years, home or overseas fees, £5k research costs (over the duration of the project). The stipend rate for academic year 2025/26 is £21,935. 

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.