Imaging, Data and Communications

Turbulent plumes, whether found in atmospheric gas dispersion or within high-energy fuel combustors, are central to applications across engineering, environmental science, and physics. These complex flows impact everything from air quality monitoring to fuel efficiency in combustion processes and are essential for fault diagnostics in aerospace propulsion. This project aims to explore whether it is feasible to image the concentration and temperature profile of gases within a turbulent plume using sparse optical measurements? Addressing this question requires modelling and analysis on how photons interact with highly dynamic media, combining principles from ill-posed inverse problems, and partial differential equations, such as radiative and heat transfer. Aside the harsh environment constraint that limits the amount of data that are feasible to collect, this endeavour is further complicated by the turbulent nature of the media itself. The challenge lies not only in reconstructing the complex features of these images from limited noisy data but also in establishing a rational way of parameterising and characterising the turbulence to suppress the uncertainty in the reconstructed images.

The research will delve into the study of random media and Bayesian inference frameworks as turbulent flows exhibit chaotic behaviour, and capturing data-consistent estimates of uncertainty will ensure the credibility of the reconstructed images. Moreover, the project offers an exciting opportunity to explore contemporary approaches in data-driven modelling of turbulence. Neural networks, including neural operators and diffusion models, show promise for efficiently representing the complex swirling and chaotic structures of turbulent flows, potentially offering an alternative or complement to traditional models.

This project provides a stimulating environment for mathematically inclined graduates, offering direct applicability to high-impact fields and the chance to contribute to pioneering research that bridges theory, computation, and practical application. Opportunities for direct collaboration with experts in gas metrology and aerospace to refine methods and validate results.

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

A first-class honors degree (or International equivalent) in Mathematics, Statistics or Computer Science, and preferably an MSc degree on a relevant topic, e.g., signal processing, data science.

An understanding of statistical inference is necessary. Experience with computational fluid dynamics/mechanics would be advantageous but not essential.

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

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

Further information and other funding options.

Sensor networks, sensor fusion and management techniques address key challenges in intelligence, surveillance, target acquisition, and reconnaissance (ISTAR). Opportunities in adaptive data-driven sensor tasking and resource management include adaptive sensor placement, adaptive waveform design to reflect the target reflection characteristics and channel environments, and adaptive sensor selection. Although these problems have solutions in specific use cases, this theme will consider scenarios with broader applications involving multiple heterogeneous sensors on single or multiple cooperative autonomous airborne platforms.

The solutions developed in this should be robust to dynamic and congested environments, adverse weather conditions, and mutual sensor interference. A range of algorithmic and signal processing or machine learning technologies will be considered, as well as specific technical challenges. For example, projects in this theme will consider aspects related to

• wide area motion imaging (WAMI), position, navigation, and timing issues (PNT);
• robustness to adversarial attack;
• sensor fusion and tracking applications;
• use of kernel and Monte Carlo methods;
• outlier-robust (and other metrics) messages in belief propagation algorithms;
• and scheduling in large dynamic networks

Probabilistic and Bayesian frameworks will be preferred to enable uncertainty quantification and management.

The techniques, solutions, and challenges proposed in this theme has applications across a range of defence and civilian applications, including search and rescue, law enforcement, and remote sensing.

This project will be jointly supervised by:

• Prof James Hopgood, James.Hopgood@ed.ac.uk
• Prof Mike Davies, Mike.Davies@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

Please note that as this is a defence based project, only UK/EU students are eligible to apply. International applicants are not eligible.

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:

   a UK applicant   an 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).

Further information and other funding options.

The Institute of Data, Imaging and Communications is seeking a PhD candidate to explore new approaches for numerical simulation using the probabilistic framework of Monte Carlo geometry processing. This research will focus on parabolic models, particularly the advection-diffusion equation that governs gas plume dispersion in congested urban settings.

Monte Carlo geometry processing methods offer a highly flexible, parallelisable, and mesh-free approach, making them appropriate for simulating PDEs in complex or dynamically changing geometries. Unlike traditional methods such as Finite Element or Finite Difference schemes that require domain discretisation, Monte Carlo approaches allow for modelling on the edge, bypassing the need to invert large and potentially ill-conditioned matrices. Originally developed for efficient rendering in computer vision, Monte Carlo methods are now being adapted to solve PDEs across a variety of fields. Leveraging recent advancements like the walk-on-spheres and walk-on-stars algorithms, designed for elliptic models, this project aims to deliver fast-converging variants of such methods for time-dependent parabolic problems.

A key challenge in this is to establish an analogue to the mean-value property in space-time domains. Moreover, whilst Monte Carlo methods offer significant advantages in accommodating dynamic boundary conditions, they also present unique challenges. Achieving fast convergence and managing statistical noise are ongoing areas of research, as these factors are crucial for applications requiring precision and computational efficiency.

This probabilistic framework also supports inverse problem-solving, such as detecting plume sources, which involves inferring release characteristics in geometrically complex environments. Synergistically, Monte Carlo methods allow for inherent uncertainty quantification, and this is particularly useful in situations with sparse measurements or stochastic model behaviours. The topic is also amenable for cross-fertilisation of ideas from randomised numerical linear algebra, exploiting the low-dimensional structure in the kernel of the advection-diffusion model.

Potential applications for this research include environmental monitoring and national security in urban settings. This project is well-suited for candidates with a strong foundation in mathematics, stochastic processes, and Monte Carlo methods.

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

A first-class honors degree in Mathematics, Statistics or Computer Science, and preferably an MSc in a closely related topic, e.g. data science or computational and applied mathematics.

A background in stochastic differential equations is necessary.

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.

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

Further information and other funding options.

Soft robotics represents a transformative approach to creating robots that are inherently safer and more adaptive to real-world environments. Unlike traditional rigid robots, soft robots can deform, stretch, and interact with humans or delicate objects in a more natural and flexible manner. As these robots become increasingly vital in fields ranging from healthcare to industrial automation, one of the key challenges is developing advanced sensing technologies that seamlessly integrate with soft, flexible materials to provide real-time feedback for precise control and decision-making.

This PhD project aims to address this challenge by developing integrated fabrication techniques that embed sensing technologies directly into soft robotic structures. By combining expertise in flexible electronics, advanced materials, and 3D-printing-based fabrication methods, this project will enable the creation of multifunctional soft robotic systems with enhanced sensing capabilities. These systems will be capable of detecting touch, pressure, deformation and other physical parameters crucial for dynamic interaction with their environments.

This project will be carried out at the SMART Group within the Institute for Imaging, Data and Communications (IDCOM) and closely collaborate with the leading robotics researchers from the Soft Systems Group. This research provides an exciting opportunity to work at the intersection of materials science, robotics, electronics and machine learning. The ideal candidate will have a background in electronics/mechanical engineering, materials science, robotics, or a related field, with a strong interest in soft robotics and sensing technologies. By joining this project, you will contribute to the rapidly growing field of soft robotics and help develop systems that could reshape healthcare, manufacturing, and other industries.

Due to a multidisciplinary nature of this project, it will be supervised by an academic team that have expertise in different engineering areas. The supervision team will consist of:

Principal supervisor:

Dr Yunjie Yang (Imaging, Data and Communications), https://www.eng.ed.ac.uk/about/people/mr-yunjie-yang

Assistan supervisors:

Professor Adam Stokes (Integrated Micro and Nano Systems): https://www.eng.ed.ac.uk/about/people/professor-adam-stokes;

Dr Francesco Giorgio-Serchi (Integrated Micro and Nano Systems): https://www.eng.ed.ac.uk/about/people/dr-francesco-giorgio-serchi

Dr Michael Chen (Bioengineering) https://www.eng.ed.ac.uk/about/people/dr-michael-xianfeng-chen

Please also note that this advert will close as soon as a suitable candidate is found. Successful candidate will be expected to start in September 2025. 

Please make sure to submit a research proposal as part of your application. For advice on writing a Research Proposal please see here:

https://www.ed.ac.uk/files/imports/fileManager/HowToWriteProposal090415.pdf

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 applicants who qualify as Home applicants* but exceptional international students will also be considered.

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

Home Students

To qualify as a Home student, you must fulfill one of the following criteria:

• You are a UK student

• You are an EU student with settled/pre-settled status who also has 3 years residency in the UK/EEA/Gibraltar/Switzerland immediately before the start of your Programme.

Further information and other funding options.

Two/three-dimensional flow-field parameters, such as species concentration, temperature and velocity are key to understand the physical and chemical behaviors of flows. The measurement data not only indicate the performance of the energy systems in many industrial sectors, such as aviation and manufacturing, but also provide experimental validation of the CFD models for R&D of the energy systems.

Laser absorption tomography (LAT) provides the unique capability of imaging thermophysical parameters of reactive flows, e.g., temperature, species concentration and velocity. LAT is implemented in a manner analogous to x-ray tomography, with the difference that, wavelength-specified incident laser beams are used rather than x-rays to acquire the absorption measurements, i.e., projection data in tomography. In industrial practice, the projection data can only be sparsely obtained from limited projection views, resulting in rank-deficient ill-posed inverse problem and thus leading to errors in image reconstructions.

This project will develop data assimilation for high-fidelity flow-field reconstruction using LAT. Data assimilation (DA) algorithms seek to solve the equations governing fluid motion subject to databased constraints. Instead of training an end-to-end neural network using simulated data, DA algorithms integrate ground-true physics, into the network, so call physics-informed neural network. Therefore, high-fidelity flow-field images are expected to be reconstructed by DA-assisted LAT. The student who joins our group will learn the fundamentals of laser absorption tomography, computational and data-driven solutions for inverse problems. The student will have a high chance of working with renowned international researchers and industrial collaborators.

Primary objectives:

1. Develop LAT model to integrate DA algorithms

2. Develop physical-informed neural networks for LAT and numerically test the network.

3. Design lab-scale experiment to validate the developed neural networks and LAT model.

4. Interpret flow-field behaviors from high-fidelity image reconstructions.

5. Improve communication and writing skills via conference presentations and journal publications.

Required skills:

1. Matlab/Python/Tensor-flow coding (mandatory)

2. Machine learning algorithms and coding (mandatory)

3. Tomography and inverse problems

4. Flow-field mechanics and dynamics

5. Optical/electronic experimental skills.

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. 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 Electronic and Computer Science or Mechanical Engineering, possibly supported by an MSc Degree.

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.

Gaseous parameters, including species concentration, temperature and velocity are key indicators of the flow-field state and hence play a crucial role in characterizing the thermochemical performance of modern carbon-free power generation systems and advanced manufacturing processes. In many industrial applications, these parameters require to be measured online with high precision and rapidness, to effectively model and active control of the dynamic thermochemical processes.

Laser absorption spectroscopy (LAS) is a non-intrusive optical sensing technology for simultaneous measurement of gas concentration, temperature and velocity. The high-speed tunability of the semi-conductor laser diodes enables the rapid measurement up to MHz. However, complex signal processing of the spectroscopic data is still an issue limiting fast and online measurement. To date, efforts have been made by using machine learning algorithms to accelerate the spectroscopic signal processing. However, most rely on (a) lab-based experimental data that fails to consider the industrial relevant conditions and (b) complicated neural network models that are impractical to be deployed on light computing units.

This project will develop hardware-accelerated laser absorption spectroscopy technology and apply it to industrial gas measurement. The student who joins our group will learn the fundamentals of laser spectroscopic sensing and hardware acceleration techniques, and will be trained for carrying out experiments in both labs and real industrial conditions. The student will have a high chance of working with renowned international researchers and industrial collaborators.

Primary objectives:

1. Develop critical thinking of industrial relevant software and hardware implementation.

2. Develop self-supervised or unsupervised neural networks to process spectroscopic signals sampled under industrial conditions.

3. Deploy the developed neural network on light-weight edge computing platforms.

4. Carry out lab-based and industrial experiments to validate the developments.

5. Improve communication and writing skills via conference presentations and journal publications.

Required skills:

1. Matlab/Python coding (mandatory)

2. Time/frequency-domain signal processing (mandatory)

3. Verilog coding and testing

4. Machine learning algorithms and coding

5. optical/electronic experimental skills.

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. 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 Electronical and Electrical Engineering or Mechanical Engineering, possibly supported by an MSc Degree.

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.

We are seeking two highly motivated PhD students to work at the intersection of academia and industry through the CHAI Hub (Causality in Healthcare AI), a newly founded EPSRC AI hub.

The CHAI Hub is a consortium of six UK universities, the NHS, industry partners, and government bodies, focused on revolutionising healthcare through causal AI.

These PhDs are in collaboration with Canon Medical Research Europe and focus on developing causal AI solutions for rethinking how we develop and use medical imaging, and contribute to an exciting area where AI meets imaging.

The candidates will join Prof. Sotirios Tsaftaris’ team [1] and CHAI [2], with regular visits to Canon Medical's Edinburgh-based R&D center [6] (part of the Canon Inc conglomerate) and potentially visit other CHAI partners. You’ll gain valuable experience working across both academic and industry environments, with strong mentorship and training opportunities, from leading experts.

Projects overview

The appearance of a medical image depends on acquisition factors such as scan modality (e.g. CT, MRI, X-Ray), scanner properties (e.g. detector size and characteristics), scan acquisition parameter choices (e.g. radiation dose), any tissue enhancement techniques (e.g. injected contrast), any phenomena giving rise to artefacts (e.g. metal implants causing streak artefact), and the position and pose of the patient. Thus, even for the same patient at the same timepoint, one image may have a different appearance to another; this variation makes it challenging for both human experts and automatic algorithms to interpret a scan.

Causal theory concerns the problem of modelling variables and their directional relationships, helping to answer questions such as: “If I change (intervene on) X, what will be the (size of) the effect on Y?” Causal models have been demonstrated in computer vision for scene understanding, to allow domain generalisation when there are changes in generative factors e.g. camera viewpoint, spatial object configuration [3]. Specifically, they have been studied in the context of deep learning on medical images, focussing on data collection, annotation, preprocessing, and learning strategies [4] with some preliminary investigation of robust learning in the presence of causal and domain-related factors [5]. In these projects we aim to model the causal relationships between scanner acquisition parameters, the subsequent acquired images, and the predictions of deep learning models trained or deployed on these images. We will additionally consider patient-related factors where available, such as prior images and clinical information.

Modelling causal relationships will enable simulations to test the robustness of deep learning solutions, as well as guiding the development of methods to mitigate the effect of these changes, either during training or deployment. Methods should be designed to learn from retrospective data; there may be opportunity to acquire new data under new conditions e.g. new combinations of scan acquisition parameter values.

Please note that this advert might close once the positions are filled. Please apply as soon as possible to avoid disappointment. 

[1] VIOS website https://vios.science

[2] CHAI website https://chai.ac.uk

[3] Anciukevicius, T., Fox-Roberts, P., Rosten, E. and Henderson, P., 2022. Unsupervised causal generative understanding of images. Advances in Neural Information Processing Systems, 35, pp.37037-37054.

[4] Castro, D.C., Walker, I. and Glocker, B., 2020. Causality matters in medical imaging. Nature Communications, 11(1), p.3673.

[5] Carloni, G., Tsaftaris, S.A. and Colantonio¹, S., CROCODILE: Causality Aids Robustness via Contrastive Disentangled LEarning. In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 6th International Workshop, UNSURE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings (p. 105). Springer Nature.

[6] Canon Medical Research Europe website https://research.eu.medical.canon

Candidates must apply via the UoE system “see the click here to apply”. Ensure that you mention the project title within your statement and in your research proposal and mention your earliest start date. The proposal does not need to be longer than two pages. The candidate can email Prof. Tsaftaris for inquiries (include a CV and mention this position in the email subject) but note that this does not constitute a formal application. The first review of applications will be 31st January 2025. There may be subsequent application review periods until the positions are filled.

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

A first class (or strong 2:1) honours degree or Distinction Masters level degree in Engineering, Computing, Mathematics, Physics, or relevant discipline is required. Candidates with an MSc equivalent training will be preferred. Demonstratable evidence of knowledge of AI (e.g. via coursework, projects, publications, work experience), and computational frameworks such as PyTorch, TensorFlow (e.g. via coursework or public repositories) are required. Evidence of prior publications of high caliber (e.g. in computer vision, image analysis or processing or machine learning) are desired but not essential criteria. The candidate should have a high level of analytical and investigative skills and a strong mathematical background. Ability to work within a team, collaborate and inspire others are essential criteria; thus, good communication and desire to own the project are sought-after abilities.

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

Tuition fees + stipend are available for Home/EU and International students

This position is fully funded for 42 months (3.5 years) and is open to all students with a preference for UK/EU nationals. International tuition fees can be covered for exceptional candidates.

Funding source: Canon Medical.

Further information and other funding options.

Tarek is a medical engineer, a final-year PhD researcher, and an Engineering Teaching Assistant at the School of Engineering. He joined Edinburgh in January 2020, completing a joint MSc in Sensor and Imaging Systems between the University of Glasgow and the University of Edinburgh.

His research project is undertaken in partnership with GlaxoSmithKline (GSK) and National Physical Laboratory (NPL) and will focus on developing image processing and machine learning techniques for evaluating disease and drug effectiveness in fibre-bundle endomicroscopy systems.

• MSc in Sensor and Imaging Systems. University of Glasgow, Scotland. 2020
• BSc in Medical Engineering. Al-Ahliyya Amman University, Jordan. 2014

Tarek is also an Academic Engagement Coordinator at the Postgraduate Institute for Measurement Science (PGI). He is actively involved in creating several PGI publications and in organising and chairing the seventh annual PGI conference 2023. (For more information email: tarek.haloubi@npl.co.uk)