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
This project will develop theory and efficient computational methods to enable radar detection in complex sensing scenarios by long-time coherent integration.
Objectives
- Develop coherent track-before-detect algorithms for different radar modes, such as range-ambiguous and Doppler-ambiguous modes.
- Investigate the errors that arise when using machine learning to predict background clutter.
- Investigate statistical detection tests and performance bounds accounting for the uncertainty in the background clutter statistics identified in objective 2.
- Investigate computationally efficient approximate schemes for low-SWAP implementations.
Description
This project will address algorithmic challenges modern multi-mode radars face in the case of hard-to-detect objects in complex background environments. It will propose coherent long-time integration (CLTI) algorithms inspired by Bayesian filtering (e.g. coherent track-before-detect) and machine learning approaches to leverage data for learning complex backgrounds.
CLTI combines evidence of object existence across measurements collected over time, thereby improving the signal-to-noise-plus-background clutter ratio (SNCR) and consequently the probability of detection. Recent works have demonstrated the benefits of coherent track-before-detect in performing CLTI and neural network models in self-supervised learning of background clutter statistics in complex environments to characterise the “null hypothesis” of the detection problem. This project aims to build upon these two strands of work and develop CLTI algorithms for different radar modes (e.g. range-ambiguous and Doppler-ambiguous modes). A key focus will be investigating statistical detection tests to use with the CLTI outputs and related performance bounds that account for errors in the background clutter statistics. These results will be used to develop computationally efficient approximations of the proposed algorithms for implementation on low size-weight-and-power (SWAP) radar systems.
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.
Further information on English language requirements for EU/Overseas applicants.
This position is fully funded.
Research Theme
Aim
This project aims to understand the physical dynamics in video through world models.
Objectives
1. Develop World Models that work in real-world data, not just in simulation, for Video Understanding
2. Develop World Models that are physically plausible
3. Develop methods that can understand cause and effect in video
Description
World Models are models that learn to link actions and consequences. They have shown excellent abilities to capture temporal dynamics for applications in robotics and video generation. However, they suffer from several issues: 1) they are trained to predict the “appearance” of the world, and therefore they may “look” accurate but be physically inaccurate: a table might bend, an object might disappear, etc. 2) they can be fairly short-term, as they are auto-regressive 3) they work mostly on synthetic scenes .
The goals of these projects are to address these three issues, with the final objective of having models that can understand temporal patterns in video at different timescales. This technology has a wide range of applications: from anomaly detection, to anticipating actions and events, or detecting previously unknown patterns in activity.
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.
Further information on English language requirements for EU/Overseas applicants.
This position is fully funded.
Research Theme
Novel Computing and Beyond-CMOS hardware
Aim
To develop a general framework for matching the memory of reservoir computers to the demands of real-world prediction and decision tasks.
Objectives
- Develop mathematical methods for estimating the multiscale linear and nonlinear temporal information required to solve a prediction or decision task from finite, noisy, and temporally dependent data.
- Develop a principled framework for comparing these task requirements with existing memory and information processing capacity descriptions of reservoir systems.
- Develop methods for quantifying uncertainty in task-reservoir alignment.
- Validate these methods and frameworks, using Dstl-informed defence and security tasks to inform reservoir selection and design.
Description
How can we determine whether a computing system remembers the right information for the task it is being asked to perform? Reservoir computing uses the dynamics of a complex system to process time-dependent data. It offers a promising framework for efficient computation at the edge, where data must be processed locally under constraints on power, communications, and computational resources. However, existing measures of reservoir memory and information-processing capacity do not yet provide a general framework for determining whether those capabilities are well matched to the temporal requirements of real-world prediction and decision task.
This PhD will develop mathematical and statistical methods for memory-task alignment in reservoir computing. The student will investigate how to identify the multiscale linear and nonlinear temporal information required by a task; characterize the corresponding information-processing capabilities of noisy and stochastic reservoirs; and develop statistically principled measures of alignment that account for finite data and system variability.
A central question will be whether these measures can predict which reservoir architectures or operating regimes are best suited to particular tasks. The methods will be tested using simulated reservoir systems and Dstl-informed defence and security problems, with potential opportunities to connect to ongoing research on novel physical computing platforms (through EPSRC funded project UKRI6477). The project combines applied mathematics, statistics, information theory, dynamical systems, and machine learning.
A strong mathematical or statistical background would be particularly suitable for this project (e.g., preparation in probability, statistics, dynamical systems, stochastic modelling, numerical methods, information theory). Prior experience in reservoir computing is not required.
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.
Further information on English language requirements for EU/Overseas applicants.
This position is funded at the Home fee rate.
Research Theme
Aim
Can you infer physical equations from video observations?
Objectives
- Establish a framework that allows computing physical equations and parameters from video data.
- Study how the framework handles adversarial attacks.
- Integrate multi-modal data into the framework.
Description
Standard computer vision can segment and label objects in a scene, but safety-critical defence and security applications require a deeper, physically grounded understanding. This PhD project addresses a fundamental question: can an autonomous sensor infer governing physical laws directly from raw observations?
Focusing on high-impact applications, such as tracking and characterising autonomous drones, this research will establish an end-to-end framework mapping raw video to segmented objects and, crucially, to their underlying differential equations. By identifying the dynamical laws that dictate how an object moves or deforms, the system will infer intrinsic physical parameters (such as mass and dimensions), classifying targets based on their physical behaviour rather than appearance alone.
Moving beyond equation discovery, the project investigates operational robustness against deceptive maneuvers. It will evaluate how the pipeline responds to adversarial physical behaviors and develop multi-modal sensor fusion, integrating complementary modalities such as radar, to mitigate these vulnerabilities.
This project will leverage physics-informed machine learning, dynamical systems, and robust multi-sensor perception to deliver explainable, tamper-resistant AI for modern defence challenges.
Preferably a background in computer science, physics, mathematics, or electrical engineering. A 1st degree preferable.
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.
Further information on English language requirements for EU/Overseas applicants.
This position is funded at the home fee rate.
Research Theme
Aim
SAR 3D Target Characterisation using Physics Informed Neural Networks
Objectives
- Develop an appropriate EM model for three-dimensional modelling of objects, accounting for propagation, reflection, and penetration.
- Reconstruct 3D SAR targets to build a foundation model for a wide range of downstream applications.
- Perform RF characterisation of 3D targets for detection, localisation, and feature extraction.
Description
The project exploits the new opportunity offered by recent high-resolution SAR systems, enabled by parabolic reflectors and electronically steered phased arrays, to extract not only the surface map of the scene but also information in the third dimension across a wide range of spectral bands, from P/L- to X-band. However, volumetric investigation of objects remains challenging due to the limited number of look angles. The project addresses this shortcoming by using prior knowledge of the physics of EM wave propagation to regularise the problem.
Access to large spaceborne SAR datasets allows us to learn a fast surrogate model of 3D wave–target interaction, based on neural operators, incorporate it into SAR imaging algorithms, and exploit all the information recorded by the instrument. The resulting 3D target information will then be fed into a machine intelligence system for localisation, characterisation, and feature extraction. While these downstream tasks often need ground-truth information, the project aims to use self-supervised learning methods and incorporate PINNs to reduce the need for labelled data as much as possible. This approach has been shown to be successful in SAR change detection using pretrained models and polarimetric SAR processing. The project builds on these results and uses manually labelled data mainly for validation.
Students with an Electrical Engineering, Physics, Mathematics or CS background will be suitable for this project.
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.
Further information on English language requirements for EU/Overseas applicants.
This position is fully funded.
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.