Electronics and Electrical Engineering

Research Associate in AI for Electron Device Design
zbhat@ed.ac.uk
1.26 Murchison House
Electronics and Electrical Engineering
Integrated Micro and Nano Systems
Postgraduate
s2704936@sms.ed.ac.uk
2.01 Alexander Graham Bell Building
Electronics and Electrical Engineering
Imaging, Data and Communications
Research Assistant in Single-photon Sensor Technology for LIDAR
Alexander.Strachan@ed.ac.uk
3.02 Scottish Microelectronics Centre
Electronics and Electrical Engineering
Integrated Micro and Nano Systems
 

Recent advances in large-array-format SPAD sensors call for novel image-processing pipelines (ISPs) to create optimised computer vision systems using these sensors.

The single-photon detection capability of SPADs, together with accompanying in-pixel computation, provides an opportunity for tailored optimisation of resolution, dynamic range, signal-to-noise ratio, and motion capture in challenging scenes.

To refine the ISP for computer vision, the resulting SPAD sensor and ISP will be deployed in a real computer vision application, such as robotic navigation, fall detection, or human tracking.

The project deliverables will be as follows:

  • A bespoke ISP optimised for single-photon input data
  • A demo system with application-specific optimisation
  • Analysis and refinement of optimal ISP partitioning and in-pixel compute
 

 

The project is supported by STMicroelectronics and will involve close engagement with STMicroelectronics Imaging Division, Edinburgh.

I. Gyongy et al., "A Direct Time-of-Flight Image Sensor With In-Pixel Surface Detection and Dynamic Vision," in IEEE Journal of Selected Topics in Quantum Electronics, vol. 30, no. 1: Single-Photon Technologies and Applications, pp. 1-11, Jan.-Feb. 2024, Art no. 3800111, doi: 10.1109/JSTQE.2023.3238520

An undergraduate degree at 2:1 or above (or international equivalent) in a relevant science or engineering discipline, possibly supported by an MSc degree.

Funding is provided for this project and is open to Home and Overseas students. 

Further information and other funding options.

On
Image
SPAD Chip Istvan Gyongy

This PhD project aims to develop a flexible, laser-based gas monitoring platform integrated within soft robotic systems for real-time detection of hazardous gases in inaccessible environments. The research will focus on advanced laser spectroscopic sensing techniques implemented in fibre-based architectures, enabling compact, lightweight, and highly sensitive gas detection. Target gases include ammonia (NH₃), hydrogen (H₂), and methane (CH₄), all of which are critical in fuel transportation and energy infrastructure due to their flammability and toxicity.

The project will explore wavelength-selective laser spectroscopic sensing for high specificity and sensitivity, alongside fibre design optimization to enhance gas diffusion, signal strength, and mechanical resilience. Integration of the sensing fibre into soft robotic platforms will be a key challenge, requiring innovative approaches to ensure flexibility, durability, and minimal performance degradation under deformation.

The envisioned system will enable soft robots to navigate confined or hazardous environments, such as pipelines, storage facilities, or industrial plants, where human access is limited or unsafe. By embedding distributed sensing capabilities directly into the robot’s structure, the platform will provide continuous, real-time monitoring of gas leaks or accumulation.

This interdisciplinary research combines photonics, soft robotics, and sensing technologies, aiming to deliver robust, scalable solutions for industrial safety and environmental monitoring. The outcomes have the potential to significantly enhance autonomous inspection systems in energy and transportation sectors.

Primary objectives:

  1. Develop fibre-based laser sensing systems for selective detection of NH₃, H₂, and CH₄
  2. Design and optimize optical fibres for enhanced gas-light interaction and sensitivity
  3. Integrate flexible, miniature sensing fibres into soft robotic platforms
  4. Achieve real-time gas monitoring in confined or inaccessible environments
  5. Improve robustness and durability of sensing systems under dynamic motions
  6. Validate system in realistic operational scenarios relevant to industrial safety

Required skills: 

  1. Background in optics or electrical engineering
  2. Experienced in optical design and signal processing
  3. Basic understanding of soft robotics or flexible systems
  4. Programming skills for data acquisition and analysis (e.g., Python, MATLAB)
  5. Signal processing and data interpretation skills
  6. Ability to work in an interdisciplinary research environment

Please note that this advert will close as soon as a suitable candidate is found.

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.

Off
Research Associate
edavies7@ed.ac.uk
2.01 Alexander Graham Bell Building
Electronics and Electrical Engineering
Imaging, Data and Communications
Postgraduate
wlaaiad@ed.ac.uk
1/A110 Alrick Building
Electronics and Electrical Engineering
Energy Systems
Research Associate
iafxenti@ed.ac.uk
1.03 Alexander Graham Bell Building
Electronics and Electrical Engineering
Imaging, Data and Communications
Postgraduate
s2950660@sms.ed.ac.uk
1/A110 Alrick Building
Electronics and Electrical Engineering
Energy Systems