Imaging, Data and Communications
Advanced electronic/optoelectronic technologies designed to allow stable, intimate integration with living organisms will accelerate progress in biomedical research; they will also serve as the foundations for new approaches in monitoring and treating diseases.
Research Theme
Aim
This PhD project aims to conduct underpinning research to address problems involving the learning of hierarchical, time-varying multi-dimensional state space models for dynamic objects/phenomena, noisy measurements made in complex backgrounds and/or in the presence of calibration errors.
Description
Dynamical system models have been the main pillar of conventional model-based approaches in control, signal processing and sensor fusion: Sensor signal processing and inference algorithms for applications such as multi-object detection and tracking, robotic simultaneous localisation and tracking (SLAM) and calibration of autonomous networked sensors are designed by combining the known physics and stochastic elements into dynamic system models. Model inaccuracies can be mitigated to achieve significant performance gains in inference and decision-making by leveraging data and model size, following the recent advances in machine learning. However, learning from data in dynamical system models to jointly address the epistemic and aleatoric uncertainties involved remains a challenge stemming from a range of factors such as noisy data and labelling, inhomogeneous sampling, model complexity and the intractability of posterior inference.
The incumbent will have the opportunity to steer the direction of the research in consideration of the impact on engineering problems, including learning models for complex backgrounds in radar detection, learning of birth and trajectory models to improve detection and tracking, or semi-supervised/unsupervised training of sensor data classifiers.
This project is fully funded.
Research theme
Novel Computing and Beyond-CMOS hardware
Aim
Validate quantum computation paradigm for information processing and sensor management
Objectives
- Quantum optimisation for sparse antenna-array design
- Joint quantum sensing and detection for RF sensing with applications in Rydberg atoms RF sensor arrays.
- Real-time Detection in electronic support measure
Description
Quantum computing is a promising new computational paradigm that researchers hope can provide meaningful computational advantage for many different problems of interest. Despite the growing interest in both these fields, a relatively unexplored area of research is how these two quantum technologies, i.e. sensing and computation, can interface to bring meaningful advantage to real world problems, more specifically can quantum computing be leveraged to improve classical and quantum sensing protocols. The aim of this project is to exploit Quantum Information Processing (QIP) and Quantum Optimisation (QO), for classical sensor management and quantum sensor information processing. The scope will be on the near- and long- term quantum computers, such as quantum annealers and quantum gate arrays, for complex optimisation problems and real-time detections, which are problems in the distributed and centralised radio frequency sensing.
This project is fully funded.
Research Theme
Aim
Our goal is to develop DNA nanostructures that are stable and functional in non-water solvents such as ionic liquids to realise a gas phase sensor.
Objectives
1. Study the melting and folding behavior of DNA structures (i.e. DNA nanostars and DNA aptamers) in ionic liquids and deep eutectic solvents using DSC and FTIR.
2. Design a DNA aptamer based system that is stable in ionic liquids to detect specific molecules (such as TNT) and study their interactions using ITC.
3. Demonstrate the capability to realise a deployable gas phase sensor based on DNA nanostructures.
Description
Functional nucleic acids such as aptamers require a solvent environment to operate. While water supports Watson–Crick hydrogen bonding, alternative solvents like deep eutectic solvents and ionic liquids offer advantages as material chassis. However, water is not passive and plays a key role in maintaining DNA structure and dynamics. This project investigates which solvent properties are necessary to preserve DNA nanostructure stability, chain dynamics, and selective molecular recognition.
For example, water with high concentrations of choline dihydrogen phosphate can inactivate nucleases while maintaining molecular function, making it suitable for labile nucleic acids. We aim to understand the biophysics of these systems to engineer solvent environments for functional DNA materials.
The target application is gas-phase sensing of compounds such as TNT for defence applications.
We will study aptamers (e.g., SRB-2) and DNA nanostars in selected solvents, evaluating signal quality and reproducibility. Ultimately, machine learning will guide solvent design for nucleic acid function and target solubility.
Full funding is available for this project.
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:
- Develop fibre-based laser sensing systems for selective detection of NH₃, H₂, and CH₄
- Design and optimize optical fibres for enhanced gas-light interaction and sensitivity
- Integrate flexible, miniature sensing fibres into soft robotic platforms
- Achieve real-time gas monitoring in confined or inaccessible environments
- Improve robustness and durability of sensing systems under dynamic motions
- Validate system in realistic operational scenarios relevant to industrial safety
Required skills:
- Background in optics or electrical engineering
- Experienced in optical design and signal processing
- Basic understanding of soft robotics or flexible systems
- Programming skills for data acquisition and analysis (e.g., Python, MATLAB)
- Signal processing and data interpretation skills
- Ability to work in an interdisciplinary research environment
Please note that this advert will close as soon as a suitable candidate is found.
- a 2:1 undergraduate degree (or equivalent) in Electronic and Computer Science or Mechanical Engineering, possibly supported by an MSc Degree.
- the University’s English language requirements
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere