Electronics and Electrical Engineering

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.

PhD in Electrical Engineering, Pontificia Universidad Catolica de Chile and University of Edinburgh, 2023.

Título de Ingeniero Civil Electricista (Electrical Engineer), Pontificia Universidad Catolica de Chile, 2016.

• Member of the Institute of Electrical and Electronic Engineers (IEEE)
• Member of the Institution of Engineering and Technology (IET)
• Next Generation Network (NGN) Member of CIGRE 

PhD in Electrical Engineering, Pontificia Universidad Catolica de Chile and University of Edinburgh, 2023.

Título de Ingeniero Civil Electricista (Electrical Engineer), Pontificia Universidad Catolica de Chile, 2016.

• Member of the Institute of Electrical and Electronic Engineers (IEEE)
• Member of the Institution of Engineering and Technology (IET)
• Next Generation Network (NGN) Member of CIGRE