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 Themes
Multi-agent Systems and Data Intelligence
Aim
Develop statistical learning methodology for active sonar that provide robust detection and classification across noisy underwater environments while producing calibrated uncertainty estimates to support reliable defence and security decision-making.
Objectives
The project is divided into an initial exploration stage, followed by one of three possible research avenues, with various possible extensions.
- Exploration. Through a review of existing literature, available data sources, and exploratory numerical experiments, investigate the most promising route for maximising the potential of statistical learning methodology in classifying active sonar data while retaining useful quantification of uncertainty.
- Methodology development. Enable transfer across frequency bands, sensor configurations, environments, and target classes, including adaptation from broad pretraining to specific operational detection tasks. Depending on the exploration stage, this may involve
- the development of a foundation model for active sonar data which can be fine-tuned to specific domains,
- a transfer learning model to exploit knowledge from one active sonar domain with large amounts of available data (such as high-frequency side scan sonar) to one with little labelled data (low frequency sonar),
- an active learning approach in which the model works only on the target domain data, first clustering it and building a statistical classifier based on human prioritisation of the detected clusters.
- Enable practically useful and useable uncertainty quantification, e.g. through Bayesian deep learning approaches or conformal prediction, focusing on uncertainty-aware classification and detection methods in high-clutter situations so that model outputs provide operationally meaningful confidence estimates and prediction sets.
Description
Active sonar is a key sensing technology for underwater defence and security, but reliable detection and classification remain challenging as acoustic returns are strongly affected by propagation conditions, reverberation, environmental variability, target aspect, and low signal-to-noise ratios. Labelled active sonar data are limited and often highly scenario-specific, making it difficult to train models that generalise reliably to new environments or sensor configurations.
Existing machine learning research in the acoustics domain focusses largely on passive monitoring due to the financial costs associated with both employing active sonar and labelling collected data.
This project will investigate statistical learning approaches for active sonar classification. The student will develop machine learning methods that learn transferable representations from large collections of underwater acoustic data, using self-supervised, weakly supervised, or simulation-assisted training where appropriate; the approach will be determined based on initial experiments, a literature review, and available data sources.
A central focus will be uncertainty quantification. The project will study methods such as Bayesian neural networks, deep ensembles, conformal prediction, calibration techniques, and probabilistic classifiers to ensure that the system outputs not only a prediction, but also an operationally meaningful confidence measure.
The probabilistic outputs of the methodology will be used in a downstream task in a practically useful way, such as flagging uncertain detections to a human operator.
A strong mathematical background and programming experience, preferably in Python, are desirable. Prior experience in machine learning, signal processing, Bayesian statistics, acoustics, or underwater sensing would be advantageous but is not essential.
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.
This position is fully funded.
In-situ optical spectroscopy during Laser Powder Bed Fusion (LPBF) captures high-resolution signals encoding the physical state of the melt pool. However, this data presents a formidable statistical challenge: observations are strongly non-i.i.d., exhibiting spatiotemporal autocorrelation, directional asymmetry from moving laser paths, and complex thermal dynamics. Because labeled defect data is rarely available at scale, this project bypasses supervised approaches to build principled, physics-informed unsupervised learning frameworks.
This project will build on the supervisors’ research in stochastic modelling, statistical learning, numerical analysis, and mathematically grounded anomaly detection. The research will focus on formulating flexible, label-free statistical machine learning for real-time anomaly detection and defect classification. Depending on candidate interest and project evolution, potential methodological avenues include combining spatial modelling with non-parametric hypothesis testing to separate systematic process variation from distributional shifts. To account for complex spatiotemporal dependencies, you will investigate robust calibration schemes (such as block-permutation or wild-bootstrap methods), while exploring techniques ranging from sparse matrix decomposition to geometric clustering and manifold learning to isolate and classify physical anomaly regimes like porosity, lack of fusion, or keyhole collapse.
Bridging spatial statistics, rough path theory, functional data analysis, and advanced process engineering, this project translates rigorous mathematical methodology into immediate industrial impact for metal additive manufacturing.
The project will be in collaboration with the LPBF team at the National Manufacturing Institute of Scotland (NMIS), and colleagues from the Department of Mathematics and Statistics at Strathclyde University.
Funding may become available on a competitive basis.
- a 1st Class undergraduate degree (or equivalent).
- 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.
Reconstructing 3D gas concentration and temperature profiles within dynamic, turbulent plumes from sparse optical measurements is a classic ill-posed inverse problem. Positioned at the intersection of radiative transfer, fluid dynamics, and statistical physics, this project aims to push the boundaries of computational imaging in extreme, harsh environments.
The research will focus on developing forward models governed by coupled radiative and heat transfer PDEs, while exploring alternative, more informative measurement modalities to significantly boost spatial resolution and noise robustness under severe data scarcity. To address the chaotic nature of dynamic flows, you will construct Bayesian inference frameworks and random media models to parameterise chaos and achieve data-consistent uncertainty quantification. Furthermore, the project offers scope to integrate scientific machine learning, such as neural operators and diffusion models, to efficiently represent complex, swirling fluid structures alongside classical physics-based inversions. Bridging deep mathematical theory with real-world applications in aerospace propulsion, combustion efficiency, and environmental monitoring, this project involves direct collaboration with specialists in gas metrology and aerospace engineering to validate computational models against experimental data.
It is ideal for ambitious candidates in Engineering, Applied Mathematics, Computational Physics, or Signal Processing with a strong foundation in differential equations, linear algebra, inverse problems, or statistical learning.
Funding may be available on a competitive basis.
- a 1st Class undergraduate degree (or equivalent).
- 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.
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.