Electronics and Electrical Engineering

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.

As AI and data centers expand, their energy demands are becoming a significant sustainability issue, with AI projected to consume 85-134 TWh of electricity by 2027—comparable to a small country's annual usage. The growing adoption of AI models like OpenAI’s ChatGPT and Google’s Bard underscores the need to reduce data centers' carbon footprints. Future AI data centers will focus on low-carbon strategies to mitigate their environmental impact.

This cross-disciplinary PhD program in electrical engineering and computer science aims to develop real-time strategies for minimizing the carbon footprint of data centers. The research will optimize the jobs scheduling at large scale data center, addressing the challenge of managing high volumes of computational tasks required by large AI models. Advanced machine learning, optimization, and data analytics techniques will be explored to enable a more proactive response to the electricity supply profile, ensuring that data center operations are effectively integrated with the broader energy network.

A key component of the program is exploring how AI data centers can support energy networks, providing grid services like frequency and demand response during periods of low-carbon electricity or when the grid requires ancillary services. This research seeks to enhance the interaction between data centers and the electricity grid, promoting better coordination and contributing to grid stability and efficiency.

The solution will focus on developing practical, low-carbon data center operations for real-world deployment. By improving demand response services, data centers can operate more sustainably while helping stabilize the grid. This research is vital for addressing the energy demands of AI systems and ensuring that future AI developments align with global sustainability goals.

To apply for this position, It will be an advantage if applicants have relevant industry or research experience, or good programming skills using Python, Julia or matlab. Strong knowledge in one or more of the following areas is highly desirable:

• Power network modelling and control

• Optimization, data science, operational research and mathematical programming.

• Machine Learning and Reinforcement Learning

• Computer science with understanding of data center structure and operation

**NOTE: There is no closing date for this position, which will remain open until filled.  Early contact is highly recommended.

Reference:

   ‘Generative AI’s Energy Problem Today Is Foundational’ https://spectrum.ieee.org/ai-energy-consumption   Misaghian, M. Saeed, et al. "Assessment of carbon-aware flexibility measures from data centres using machine learning." IEEE Transactions on Industry Applications 59.1 (2022): 70-80.   Sarkar, Soumyendu, et al. "Real-time Carbon Footprint Minimization in Sustainable Data Centers with Reinforcement Learning." NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning. 2023.

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

Interested applicants are welcome to contact Dr Wei Sun by email for pre-application enquiries (W.Sun@ed.ac.uk).

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in Electrical Engineering, Computer Science, Mathematics, or related areas. 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.

The Centre for Electronics Frontiers (CEF), led by Regius Chair of Engineering Prof Prodromakis, brings together diverse and interdisciplinary expertise for transforming modern society through technology. Our ambition is to push the frontiers of electronics through emerging technologies, disrupting current ways of thinking by innovating advanced nano/biosensors, safe and efficient energy storage solutions and novel hardware for AI. We are offering prospective PhD students the opportunity to join our team, interested in devoting their passion for addressing some of the challenges we have identified. This project will also be supervised by Dr Cristian Sestito.

The project aims at building accelerators based on Field Programmable Gate Arrays (FPGAs) and suitable to deliver computer vision tasks through Generative AI. Generative Adversarial Networks (GANs) based on Convolutional Neural Networks (CNNs) are promising candidates in this direction: they exploit adversarial learning and feature extraction to execute a multitude of applications, including image dataset generation, image-to-image translation, face frontalisation. Specifically, the project targets deploying applications like this on FPGA-based Systems-on-Chip (SoCs) to be showcased in real-time systems, with an in-depth investigation on optimisation techniques to reach high throughput and low energy footprint (e.g., data quantisation and pruning). This will require preliminary training using software frameworks, like PyTorch or TensorFlow.

The required skills are as follows:

• Knowledge and expertise on FPGA design for AI using Verilog/VHDL (mandatory).
• Knowledge and expertise on training and testing CNNs using SW frameworks, like PyTorch or TensorFlow (mandatory).
• Basic knowledge on Systems-on-Chip based on FPGAs (desirable).
• Previous experience on using 3rd party IP cores for vision applications (desirable).
• Previous experience on training generative AI models (desirable).

Group website: https://cef.eng.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. 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.

To qualify as a Home student, you must fulfil 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. (International students not eligible).

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.

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