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 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 Theme
Multi-agent Systems and Data Intelligence
Aim
To develop a neuro-algebraic multi-agent digital twin for 6G network subsystems and individual network elements to monitor, analyse, certify, and mitigate safety and cybersecurity threats.
Objectives
- To develop an architecture for interactive multi-agent monitoring and analysis of the states of 6G network subsystems, aimed at detecting, analysing, and predicting hazardous situations, anomaly behaviour, and potential intrusions.
- To develop a technology for the formalisation of telecommunication network elements and subsystems as algebraic specifications, enabling symbolic (algebraic) modelling, verification, and reasoning about their behaviour and interactions.
- To develop methods for analysing and responding to anomalous network activity, with the aim of identifying critical situations and supporting timely mitigation of safety and cybersecurity threats.
- To develop an agent architecture based on the interaction between algebraic and neural components, enabling the combination of formal reasoning, data-driven learning, behavioural prediction, and adaptive decision-making.
Description
The project addresses the development of an architecture of interacting agents that operates as a digital twin of a 6G telecommunication network. The proposed system continuously observes the states of network elements and subsystems in real time. Each agent has access to a virtual representation of the network component it supervises, described as an algebraic digital twin that captures structural, behavioural, and interaction properties. In addition, each agent includes a neural component trained on normal behaviour patterns of the corresponding 6G subsystem or element.
By analysing real-time observations using learned behavioural models and verifying them through the algebraic component, agents can detect deviations, unsafe states, anomalous activity, or potential intrusions. When undesirable or dangerous behaviour is identified, the affected agent communicates with other agents and generates coordinated responses. These responses aim to support timely mitigation, neutralisation of attacks, and assurance of safe and secure operation.
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 funded at the Home fee rate.
Research Themes
Aim
Develop multimodal virtual sensing methods that leverage data-driven models to infer rich sensory feedback from minimal physical inputs, thereby enabling adaptive and reliable robotic manipulation in cost-constrained and hazardous environments.
Objectives
- Develop AI-based virtual sensor models that can approximate multimodal feedback (vision, haptics, force-torque, audio) from limited real sensor inputs.
- Design and evaluate multimodal fusion strategies for learning robust object property and interaction representations that generalize across tasks and environments.
- Investigate transfer learning and domain adaptation methods to enable deployment of virtual sensors trained on simulation or rich offline datasets to real-world robotic platforms.
- Validate virtual sensing for manipulation tasks by benchmarking performance against fully instrumented systems in both controlled and hazardous/constrained scenarios.
Description
The PhD project investigates virtual sensing for robotic manipulation, focusing on the use of data-driven models to approximate multimodal sensory feedback. The core objective is to train AI models on rich sensory datasets (e.g., vision, haptics, force-torque, proprioception, audio) to learn robust representations of object properties and interaction dynamics. At deployment, these models will infer missing modalities from minimal physical sensing, enabling reliable manipulation in cost-constrained or hazardous environments. Key research challenges include multimodal fusion, domain adaptation, and the transfer of representations from simulation or offline data to real-world robotic systems. The project aims to advance theoretical understanding of virtual sensing architectures while delivering practical methods for adaptive, resource-efficient robotic manipulation.
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 Theme
Novel Computing and Beyond-CMOS hardware
Aim
This project seeks to develop high performance, non-linear metamaterials by harnessing the atomistic resolution of self-assembling DNA nanostructures.
Objectives
- Further development of DNA origami based metamaterials to deliver efficient frequency shifting structures to convert MWIR and NIR light into the visible region;
- Characterise the performance and robustness of these metamaterials;
- And develop methods to integrate the metamaterials into carrier materials to facilitate manufacturing.
Description
The Mk1 eyeball is a remarkable and delicate organ. Vision is our primary sensing modality in many environments, and eye protection from foreign bodies or high-energy radiation is vital. This provides an opportunity space for the development of technologies to integrate enhanced sensing into eyeware. The ability to be able to perceive radiation in currently invisible regions of the electromagnetic spectrum could create significant advantage in threat identification and target acquisition. Non-linear (up-conversion) metamaterials are able to frequency shift infrared light into the visible region. Integrating such materials into eyeware could enable the passive perception of heat signatures and laser-illuminated objects. This project seeks to build on our ability to fabricate metamaterials from DNA nanotechnology. We will design, build and test metamaterial up-conversion structures to understand their performance and robustness. We shall develop strategies to embed our metamaterials in, or coat them onto, macroscale devices for further assessment as potential materials for low-cost eyeware.
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.