Electronics and Electrical Engineering

Soft Systems Group website

• BEng(hons), MRes, PhD

• Member of the Royal Society of Edinburgh Young Academy of Scotland (MYAS)

• Programme Director: MSc Electronics

https://elliotjcrowley.com

• DPhil Engineering Science, University of Oxford
• MEng Engineering Science, University of Oxford

• Machine Learning and Data Analysis
• Programming Skills for Engineers
• Signals and Communications Systems 2 

• Simplifying machine learning
• Automated machine learning
• Low-resource deep learning
• Engineering applications of machine learning

Dr Julianna Panidi is a Chancellor’s Fellow/Lecturer in n Climate and Environmental Sustainability at the Institute for Integrated Micro and Nano Systems at the School of Engineering. Before she was an EPSRC David Clarke Fellow in the Department of Chemistry at Imperial College London. She is a Fellow of the 2024 European Talent Academy and a Fellow of the Higher Education Academy, and she has been a Mental Health First Aider for over 9 years.

Julianna obtained her PhD in 2020 from Imperial College London, Department of Physics, part of the Plastic Electronics CDT. Additionally, she holds an MRes in Physics and Nanomaterials (2015) from the University of Pierre and Marie Currie in Paris, France. In 2014, she completed her BSc in Materials Science at the University of Patras in Greece.

Accepting PhD applications.

• Chancellor's Fellow, 2024, University of Edinburgh
• Fellow of the 2024 European Talent Academy, Imperial
• Postdoctoral EPSRC Fellowship, 2022
• David Clarke EPSRC Fellow, 2022

• Co-Chair of the People & Culture Committee at the School of Engineering, UoE, since 2025
• Member of the Athena Swan SAT, UoE, since 2025
• Fellow of the Higher Education Academy, 2023
• Member of the Royal Society of Chemistry, 2022

• Professional Issues 4
• Cohort Lead Year 5

Her research focuses on developing high-performing and eco-friendly solution-processed electronics, such as thin film transistors, sensors, and solar cells. She has studied methods to enhance the optoelectronic properties of the materials and the devices. During her DCF fellowship, she was focusing on sustainable solution-processed solar cells, primarily focusing on materials, methods, and solvents used during manufacturing.

Visit the Sustainable Printable Electronics Research Group website website

Google Scholar

Dr Chang Liu received the B.Sc. degree in automation from Tianjin University, China, in 2010, and the Ph.D. degree in testing, measurement technology and instrument in Beihang University, China, in 2016. From April 2016 to January 2018, he was a postdoctoral researcher in the department of air pollution and environmental technology, Empa-Swiss Federal Laboratories for Materials Science and Technology (ETH Domain), Dübendorf, Switzerland. He is now a Senior Lecturer in the Agile Tomography Group at the School of Engineering, University of Edinburgh.

Dr Liu’s current research interests include laser spectroscopy, laser imaging, data-driven imaging techniques and their applications to reacting flow-fields diagnostics and environmental monitoring. His expertise is in design of near/mid infrared LAS sensing systems, and development of high-sensitivity and data-driven laser imaging methodologies. It covers fundamental spectroscopic modeling, inverse problem solving, machine learning, hardware acceleration, signal processing and embedded system. In collaboration with worldwide industrial and academic partners, Dr Liu and his team focus on providing cutting-edge laser-based sensing solutions for various challenging problems in both the industry and academia.

• 2016 Doctor of Philosophy (Ph.D.), School of Instrumentation and Opto-Electronic Engineering, Beihang University, Beijing, China.
• 2010 Bachelor of Science (B.Sc.), School of Electrical Engineering and Automation, Tianjin University, Tianjin, China

• IEEE Senior Member

• Digital System Design 4 (ELEE10007)
• Analogue Circuits 3 (ELEE09026)
• Digital System Design and Digital Systems Laboratory 3 (ELEE09035)
• Digital System Design 2 (ELEE08015)

• Data-driven imaging towards high spatial/temporal resolution
• Cutting-edge laser-based sensing solutions
• Sensor design for ultra-weak optical and electrical signals detection
• Embedded system design and hardware/software interface

Research Opportunities

Ph.D. scholarships

• Edinburgh Global Research Scholarship
• Principal's Career Development PhD Scholarships
• Carnegie PhD Scholarships
• China Scholarships Council/University of Edinburgh Scholarships

Information on funding opportunities and tuition fees can be found here.

Postdoctoral Research Associate (PDRA)

• EPSRC Fellowships
• Newton International Fellowships
• Marie Curie Individual Fellowships
• Fellowships and Engineering for Development Research Fellowships (Royal Academy of Engineering Research)

Google Scholar

I am an Electrical Engineer interested in the fitting electromagnetic data to their respective models in the context of tomographic image reconstruction and model parameter estimation. I was educated in the UK and held research positions at School of Maths at the Univeristy of Manchester, the Lab for Information and Decision Systems at MIT and the Energy, Environment and Water Research Centre of the Cyprus Institute. In Edinburgh I lead the Agile Tomography Group that specialises in low-frequency electromagnetic simulation and tomographic image reconstruction, as well as chemical species tomography from spectroscopic measurements of light in the near infrared regime.

My research is in the realm of applied inverse problems and this usually entails, in some proportion: mathematical modelling, signal processing, statistical estimation and optimisation algorithms. As of 2016 I am also a faculty fellow at newly established Alan Turing Institute, which perhaps also qualifies me as a data scientist. My research is relevant to applications of electromagnetic imaging in geophysical exploration, industrial process tomography, biomedical imaging and non-destructive testing of materials and structures. In particular, I am interested in computational approaches suitable for large-scale pde models for static and low-frequency electromagnetic fields and algorithms that process these models along with measurements in the quest to image the electromagnetic properties of a domain of interest.

Nick Polydorides

(Office: 2.10 Alexander Graham Bell)

• Ph.D in Electrical tomography, in 2002 from UMIST (now The University of Manchester) with Bill Lionheart and Hugh McCann
• MSc in Computation, in 1999 from the University of Oxford
• BEng in Electrical Engineering and Electronics, in 1998 from UMIST (1st class Honors)

I am a member of IEEE Signal Processing and SIAM Imaging societies.

I am involved at the teaching of Engineering Mathematics 2A which involves mostly Laplace and Fourier series solutions of ordinary differential equations, which are essential elements of signal processing and understanding physical phenomena such as the propagation of sound, heat diffusion etc. I also take great pleasure from teaching the vector calculus and integration course in Engineering Mathematics 2B as it provides a good deal of insight to may of the physical phenomena us engineers need to understand. As for a teaching philosophy, I see myself standing in between the knowledge and the students, and although I cannot take the one to the other, I see my role as trying to make the distance look smaller.

Other than electromagnetics I have developed an interest in fluid-structure interaction models. This typically leads to a cluster of mechnical inverse problems relevant to the offshore energy exploration with towed arrays.

I am constantly looking to hire talented students who want to pursue a PhD because they want to make an impact to a certain application. If you have a numerate background and an appropriate course average from your BEng, BSc or MSc then I would like to hear from you at the email address above. Opportunities may also arise in the context of the Alan Turing Institute if your proposed theme falls within the so-called 'data-centric engineering'.