Electronics and Electrical Engineering
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
To develop a combined radar cross-section and synthetic aperture radar machine learning based classifier.
Objectives
1. Adopt data fusion approaches combining RCS and ISAR information for the studied targets and for the various polarization cases with machine-learning (ML) based methods for target classification.
2. Assess classifier robustness with respect to aspect angle, noise levels, data sampling variations, target positional information, and potential reduced information scenarios.
3. Develop a preliminary data set using simulation, polarization-specific, and measured data of relevant targets while using measurement and modelling facilities within The Scottish Microelectronics Centre (SMC).
Description
There is an increasing defense requirement for classification techniques for airborne, terrestrial, and seaborne targets. Typical radar cross-section (RCS) signatures provides information on the electromagnetic scattering of targets while synthetic aperture radar (SAR) provides a two-dimensional representation of the distribution of scattering features in terms of 2D images. These two forms of information are complementary yet different: RCS signatures and measurements describe the aggregate scattering response under particular observation conditions and polarization states, whereas SAR can provide relative spatial details on the scattering features of targets.
The PhD research work will aim to investigate a machine-learning (ML) framework for classifying a broad range of airborne, terrestrial, and sea-borne targets using both RCS signatures and ISAR representations. The main motivation will be to assess if non-imaging-type RCS signatures along with SAR-derived information, can provide more robust classification of targets, and compared to when these representations are independently examined. Numerical modelling, full-wave simulations using commercial software tools, and measurement information strategies will be adopted while using the existing facilities within the RF lab at UoE.
An engineering or physics background is suitable, but engineering is the preference.
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.
Research Themes
Aim
Develop an integrated probabilistic decision-support system for detecting and evaluating the origin of nefarious microbial threats.
Objectives
- Fusion of diverse data sources (e.g. observations, biological, genetic) into an integrated system.
- Modelling and propagation of uncertainties, from quantitative data (e.g. measurement uncertainty), subjective information (e.g. intelligence data) and uncertainties in the possible origin or dissemination of pathogenic microbes.
- Development of a fast, usable and generalisable graphical decision support system for supporting real-time and uncertainty-aware decisions.
Description
The aim of this project is to integrate Bayesian probabilistic/AI graphical modelling approaches with diverse datasets to assess and characterise microbial threats - combining subjective and background information, genomic data, proteomic and toxin data, and microbial growth data to provide a comprehensive and dynamic view of pathogenic microbial encounters. The research will develop probabilistic methods for distinguishing between natural, intentional or accidental outbreaks of pathogenic microbes – this is crucial for understanding the current threat from bioweapons.
In some cases, the question of nefarious intent might be answered almost definitively by a specific type of omics-based science, such as in the case where very clear genetic manipulations are evident in a microbe’s DNA. However, in many other cases, the proposition of nefarious intent will need to be assessed in probabilistic terms and there will be a requirement to incorporate a diverse suite of non-definitive but corroborating information in one model. Bayesian graphical modelling approaches such as Bayesian Networks and Chain Event Graphs provide an appropriate framework for integrating this information and have been used successfully in a forensic science context.
The challenges to be addressed by the project include applying these new approaches to real-world decision-making processes, adapting models for different pathogens in real-time investigative situations, and incorporating complex and highly diverse biological and contextual information.
Some background and interest in statistics or probability, interest in applying mathematical methods to biology.
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.
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.
Developing renewable energy solutions that can be rapidly implemented in the market using eco-friendly materials and manufacturing methods is crucial. Among various renewable technologies, photovoltaics have significant potential to support climate change mitigation. Organic photovoltaics (OPVs) have recently attracted considerable attention due to a new family of semiconductors that enable highly efficient light harvesting in both indoor and outdoor environments. Additionally, OPVs offer a low carbon footprint and high recyclability potential.
However, a current limitation is the use of toxic solvents and materials in manufacturing. Most organic electronic devices require halogenated and non-halogenated aromatic solvents, which are often carcinogenic or toxic to human reproductive systems and harm the environment. For large-scale production and commercialization, this is a critical issue.
This project aims to enhance the performance of OPVs through engineering strategies, eliminate the use of toxic materials and implement methods to enhance their stability. In addition, thin films and OPVs will be evaluated with a series of optoelectronic and morphological characterisation tools.
The PhD candidate will be supervised by Dr Julianna Panidi (School of Engineering) and Dr Yue Hu (School of Chemistry).
The successful candidates will join our team, which includes researchers from the Institute for Integrated Micro and Nano Systems, the School of Chemistry, and the wider College of Science and Engineering.
Eligibility
- a 2:1 undergraduate degree (or equivalent).
- applicant's must meet the University’s English language requirements.
Before you apply: We strongly recommend that you contact the supervisor for this project before you apply.
https://eng.ed.ac.uk/sustainable-printable-electronics-research-group
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.
Funding may be available to Home students on a competitive basis. Candidates are invited to discuss potential funding opportunities.
Research Theme
Aim
This EngD project will investigate the implementation of real-time signal processing algorithms within FPGA-GPU processing pipelines.
Objectives
1. Investigate the suitability of GPGPU architectures for streaming signal processing workloads by implementing and evaluating adaptive radar processing algorithms. This should include assessments on throughput, latency, and energy efficiency compared to multi-core CPU implementations.
2. Design, implement, and evaluate a software product line (SPL) architecture for signal processing applications. The developed architecture should support configurable processing chains and efficient exploitation of heterogeneous compute, while optimising for performance, maintainability, and portability.
3. Investigate how heterogeneous SPL architectures can be verified to meet the objectives and assurance requirements of DO-178C, identifying any architectural and verification practices necessary.
Description
As the benefits of Moore's Law continue to diminish, the computational demands of modern signal processing systems are increasingly outpacing the performance gains available from conventional CPU architectures. While multi-core processors have extended the life of CPU-based solutions, many real-time applications must now process larger volumes of data under increasingly demanding latency constraints.
In airborne sensing systems, large quantities of in-phase and quadrature (IQ) data are generated by FPGA-based processing chains. Traditionally, this data is processed on general-purpose CPUs; however, many signal processing algorithms are highly parallel in nature and may be better suited to Graphics Processing Units (GPUs). The success of GPUs in fields such as AI and scientific computing has shown their ability to deliver significant improvements in throughput and processing performance, raising the question of how applicable they are to real-time signal processing.
Early investigations have shown promising results. At NVIDIA GTC 2025, SAAB presented a GPU-accelerated signal processing chain implemented using CUDA, reporting approximately a tenfold improvement over an equivalent CPU implementation. In parallel, NVIDIA's Holoscan platform is enabling high-rate sensor data to be streamed directly from FPGA hardware to GPU for processing pipelines. While this opens up new possibilities for real-time processing, it also raises questions around performance predictability, system integration, verification and software assurance, all of which are important considerations for flight-qualified systems.
This EngD project will investigate the implementation of real-time signal processing algorithms within FPGA-GPU processing pipelines. Using NVIDIA Holoscan as the underlying framework, the research will evaluate the performance and latency characteristics of GPU-accelerated processing while exploring the verification and validation challenges associated with deploying such technology in airborne systems. The aim is to better understand both the benefits and the practical barriers to adopting GPUs within future real-time sensing platforms.
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.