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 fast randomised adaptive processing algorithms for next-generation digital-array airborne radar, with performance guarantees stated in operational detection terms rather than abstract mathematical error bounds.
Objectives
- Determine how far the adaptive processing can be sketched or compressed without missing targets, with certified detection performance.
- Automatically detect and remove misleading or contaminated training data.
- Assess and enhance the algorithm's robustness to jamming.
- Validate the algorithms on open real radar data against standard simulated baselines.
Description
Next-generation airborne radars digitise every antenna element. As a result, the adaptive filters used to detect slow-moving ground vehicles (Space-Time Adaptive Processing, STAP) must now handle covariance matrices of dimension close to a million, recomputed every few milliseconds. Classical theory demands roughly twice that many statistically identical training samples, which real, heterogeneous terrain cannot supply. Fortunately, ground clutter is strongly low-rank, which is exactly the regime where randomised numerical linear algebra excels.
This project develops randomised Nyström STAP algorithms whose key novelty is certification. Instead of matrix-norm error bounds (tail concentration), guarantees will be expressed in radar context terms that are intuitive to radar engineers, e.g. probability of detection at a fixed false-alarm rate, and minimum detectable velocity. These will be delivered in conjunction to randomised sketch size, training budget and latency.
The student will also use statistical leverage scores and diversity-promoting sampling to screen contaminated training data without the circular need for a covariance inverse. Depending on progress, a further strand could investigate how re-randomising the sketch every processing interval makes the adaptive response unpredictable to a jammer. Methods will be validated on benchmark datasets such as KASSPER/DARPA against established baselines. The outcome is an assurable alternative to learned methods for ISR aircraft operating in cluttered, contested electromagnetic environments.
A strong background in linear algebra, probability and statistics.
To reduce risk the desired PhD candidate should have a first-class degree in Mathematics with a solid foundation in numerical methods, and programming. An MSc in a numerate discipline will be considered an advantage.
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 use AI/ML methods to adapt and optimise the PNT solution in the absence of reliable GNSS for varying sensor suites in varying environments, whilst remaining agnostic regarding inputs and platform type.
Objectives
- Obtain sufficient labelled data, real and synthetic, for PNT from sensor systems in varying platforms and environments to apply Machine Learning (ML) techniques to.
- Identify suitable AI/ML methods to be applied to the data with realistic metrics as to what constitutes a “good” solution regardless of environmental and sensor suit constraints.
- Develop the selected methods on the sensor and digital world-model data available to provide system level PNT solutions for varying GNSS conditions and platform types.
- Validate the methods against independent data with GNSS “truth” available.
Description
Positioning, Navigation and Timing (PNT) without reliable GNSS is a complex problem for which a wide variety of solutions are being developed, from signals of opportunity to mutli-sensor fusion. What methods can be applied vary significantly depending on the platform type and the sensor suite available on that platform and with the environment the sensors and platform operate in. Moreover, the sensor information available may vary even within a mission depending on what other tasks the sensors have to undertake and the environment (physical and electromagnetic) may change over the course of the mission. Ensuring some level of agnosticism enables a solution to maintain a highly accurate output level, giving the AI/ML solution a wider use case. Varying input types and numbers of is to be considered, as to not cause any sensor suit constraints for the wider platform. The information the sensors provide, must also be referenced against the “worldview” that the AI/ML has been trained on e.g. digital terrain models, locations of features, emitters, etc, to provide and alternative to GNSS in a global reference frame. These models can also be used to determine which alternative PNT methods are optimum in specific environments. Building on work begun on other SPADS CDT projects and making use of data available, the project will look at how ML can be used to train a PNT solution to use different data sources over the mission to meet the required accuracy of PNT for the mission to be carried out successfully and to learn from previous missions to improve PNT performance over time. The system will not be concerned with GNSS signal validity and will aim to be ‘activated’ as a primary navigation system upon GNSS interferences. This means the system should be able to operate using all levels of GNSS available, providing the most notable PNT accuracy increase.
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.