Imaging, Data and Communications
Research Theme
Aim
Develop new computationally efficient algorithms for large multi-dimensional array processing.
Objectives
- Develop new fast beamforming algorithms and adaptive array processing techniques using advances in fast Fourier integral operators and randomized algorithms
- Build applications using these techniques for high-dimensional wideband sensor array processing problems, e.g. requiring the simultaneous processing of range, doppler, azimuth, elevation and frequency, in challenging acoustic and/or radar domains.
- Explore the performance trade-off between accuracy and computational efficiency.
Description
Large scale, multi-dimensional array processing problems, e.g. simultaneously processing range, doppler, azimuth, elevation and frequency, are ubiquitous in radar and sonar sensing, and it is essential to keep the computation to a minimum in order to process the outputs in a timely manner. For narrowband uniformly spaced linear arrays fast transforms such as the FFT can be exploited. However, dealing with large wideband non-uniform arrays remains a major challenge.
This project will develop novel fast array processing algorithms for statistical estimation and beamforming based on two recent developments in signal processing and related mathematics. The first is the construction of fast Fourier integral operator approximations [1,2,3,4], that can provably approximate the Fourier integral to a given level of accuracy in O(n.log(n)) while accommodating different sampling/physical geometries. The second is efficient fast randomized algorithms for solving large least square problems [5,6] that offer the potential for novel beamforming solutions [7]. The project will explore how these ideas can be integrated to develop novel efficient array processing solutions to tackle challenging sonar and RF array processing problems and to assess their performance trade-off between accuracy and computation.
[1] E. Candes, L. Demanet, & L. Ying, 2007, Fast Computation of Fourier Integral Operators. SIAM Journal on Scientific Computing, Vol. 29, Iss. 6, pp. 2464-2493.
[2] L. Demanet, M. Ferrara, N. Maxwell, J. Poulson, and L. Ying, 2012, A butterfly algorithm for synthetic aperture radar imaging. SIAM J. Imag. Sci., vol. 5, no. 1, pp. 203–243.
[3] S. I. Kelly and M. E. Davies, 2014, A fast decimation-in-image back-projection algorithm for SAR. 2014 IEEE Radar Conference, pp. 1046-1051.
[4] S. I. Kelly, M. E. Davies, J. S. Thompson, 2014, Parallel Processing of the Fast Decimation-in-image Back-projection Algorithm for SAR. 2014 Sensor Signal Procesing for Defence (SSPD), pp. 1-5.
[5] V. Rokhlin and M. Tygert, 2008, A fast randomized algorithm for overdetermined linear least-squares regression. Proceedings of the National Academy of Sciences, 105(36), pp 13212–13217.
[6] P.-G. Martinsson and J. A. Tropp, 2020, Randomized numerical linear algebra: Foundations and algorithms. Acta Numerica, 29, 403–572.
[7] R. S. Srinivasa, M. A. Davenport and J. Romberg, 2019, Trading Beams for Bandwidth: Imaging with Randomized Beamforming. SIAM J. Imag. Sci., vol. 13, no. 1, pp. 317-350.
Industry Partner
This project is co-funded by Thales UK.
Applications
First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.
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 project requires a student with a high level of mathematics or numeracy.
Further information on English language requirements for EU/Overseas applicants.
Full funding is available for this position.
Research Theme
Aim
To develop machine learning algorithms for the robust detection and classification of natural and man-made objects in the ocean environment.
Objectives
- Leverage unsupervised learning to understand the background ocean environment as a reference for detecting objects.
- Develop a few-shot learning approach to classify the acoustic signals emitted by marine mammals and vessels travelling on the surface of the sea.
- Explore strategies for improving the robustness of underwater acoustic models to distribution shifts.
Description
Passive sonar is a key sensing modality for achieving understanding of the ocean environment, to help protect naval platforms. This technology can be applied in a range of systems, from hull-mounted or towed hydrophone arrays to sonobuoys or uncrewed underwater vehicles. Sonar contact classification is a challenge, due to the need to detect, label, and track multiple targets. Much of this work is currently carried out manually by sonar operators. The growing risk from increasingly stealthy targets, complex environments, and a data deluge from more capable sensors with more channels, necessitates new automatic approaches to marine object detection and localization.
Recent developments in the fields of artificial intelligence (AI) and machine learning (ML) offer promise for improvements in the analysis of acoustic signals, in terms of speed, accuracy, and robustness. This project will investigate novel approaches to analyzing the data and provide an increased understanding of the maritime arena.
In later years of the PhD, QinetiQ would like to investigate the possibility of an internship for the student for up to 6 months.
Applications
First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.
QinetiQ has developed an algorithm for detecting the time stamps of sound cuts of short mammal calls in the master tapes of the Watkins Marine Mammal Sound Database. This enables the development and assessment of object detection algorithms as opposed to just classification of sound cuts, which is what the database is usually used for. The algorithm can be made available to the student to use and improve on.
QinetiQ has access to unclassified recordings of surface vessels. Subject to permission of the data owner, these could be made available to the student to support the research.
QinetiQ: https://www.qinetiq.com
Watkins: https://whoicf2.whoi.edu/science/B/whalesounds/index.cfm
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.
Full funding is available for this position.
Research Theme
Multi Agent Systems and Data Intelligence
Aim
To develop a principled framework for representing, communicating, and aggregating uncertainty in large language model (LLM)–based agent systems, thus improving coordination, calibration and safety in multi-agent reasoning.
Objectives
- Elicit Uncertainty: Develop and compare methods for extracting calibrated uncertainty estimates from individual LLMs.
- Represent & Communicate: Design message formats that carry uncertainty information between agents (e.g., probabilistic statements, confidence intervals, prediction sets).
- Propagate & Aggregate: Establish mathematically sound rules for combining and updating uncertainty as messages pass between agents.
- Evaluate Impact: Create benchmarks and metrics to quantify how uncertainty-aware communication affects accuracy, calibration, and robustness of multi-agent systems.
Description
State of the art solutions in many complex problem domains are often now “Agentic” systems in which collaborations of specialist LLM-based agents collectively solve problems. For example, in cyber-security, surveillance agents may monitor logs to detect potential exploits, and cooperate with patching agents that fix vulnerabilities, and testing agents that validate the results. In such systems agents communicate through natural-language messages that reflect single “best guesses.” In probabilistic terms, these are point estimates of a model’s belief. As messages are exchanged, uncertainty information is lost, leading to over-confidence, error cascades, and poor coordination under ambiguity.
This project investigates how to restore and maintain epistemic and aleatoric uncertainty throughout an agentic pipeline. The first stage will focus on discovering the most reliable way to elicit and perceive uncertainty estimates with single LLMs. The second stage will formalize uncertainty-aware message formats—for example, by attaching confidence scores, probabilistic distributions, or prediction sets to text outputs. The third stage will develop algorithms for propagating and aggregating uncertainties across agents (eg Bayesian, Dempster-Shafer, conformal fusion). Finally, we will build benchmarks that measure team-level uncertainty calibration. Expected outcomes include (i) theoretical understanding of uncertainty composition in LLM collectives, (ii) practical mechanisms for safer, more transparent AI collaboration, and (iii) open-source tools for uncertainty calibration and propagation in agentic frameworks.
Applications
First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.
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.
Full funding is available for this position.
Research Themes:
Aim
We will develop fast hardware to deploy real-time Bayesian experiment design to optimise the performance of quantum sensors
Objectives
- Integrate a GPU with a programmable real0time arbitrary waveform generator (QuantumMAchines OPX+) to create a real-time control system capable of fast Bayesian inference and experiment design, with millisecond-scale latency
- Integration of hybrid “grey-box” models (combining an analytical model with data-driven inclusion of imperfections) with Bayesian experiment design and deployment on the hardware system in (1)
- Benchmarking of the system of different quantum sensing modalities
Description
Quantum sensors represent the generation-after-the-next technology for sensing, exploiting the properties of quantum particles (such as the spin of a single electron) to achieve atomic-scale spatial resolution and/or unprecedented sensitivities.
Despite the demonstrated advantages of quantum sensors in applications ranging from defense to healthcare, they are often limited by weak signal (resulting in slow data acquisition and low bandwidth), fragile operation in well-controlled laboratory environment and difficulty of use by non-experts.
This project will develop world-first adaptive quantum control hardware, enabling the generation of microwave quantum control sequences with microsecond latency based on GPU-accelerated Bayesian inference and experiment design. The system will deploy real-time Bayesian experiment design to optimize the performance of a spin-based quantum sensor in terms of speed and accuracy, and integrating hybrid analytical/data-driven (grey-box) approaches to improve robustness in hostile and changing environment. Our work will enable the deployment of quantum sensors in real-world applications, such as defense, and could be further extended to different types of “classical” sensors.
Applications
First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.
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.
Home rate fees and stipend are available for this position.
Research Theme
Aim
Develop new particle filter solutions for tracking of interacting multiple targets.
Objectives
- Develop new interacting particle filters that can tackle multi-target tracking in a scalable manner
- Understand the hidden low dimensional structures within multi-target systems to enable high dimensional scalable Monte Carlo filtering
- Explore how these models can further be used to predict intent for groups of cooperating and/or adversarial targets.
Description
High-dimensional multi-target tracking poses severe computational challenges for sequential inference. Although particle filters provide a principled Monte Carlo framework, their efficiency collapses with dimension. This project aims to identify and exploit hidden low-dimensional structures within multi-target systems, enabling adaptive filtering methods that retain Monte Carlo flexibility while mitigating the curse of dimensionality. We will develop local particle filters, block-based filtering, and topology discovery methods to automatically detect weakly interacting groups of targets. We may consider scenarios involving tens to a few hundred targets. At this stage, we will primarily use simulated data, with the flexibility to incorporate real data from partners in later stages. The outcome will be scalable algorithms for structured high-dimensional tracking, combining statistical efficiency with real-time feasibility in sensing and defence applications.
Applications
First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.
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 project requires a student with a high level of mathematics or numeracy.
Further information on English language requirements for EU/Overseas applicants.
Full funding is available for this project.
Research Themes
Aim
To develop a software and hardware system that uses real and predicted motion data to simulate how mobile satellite terminals behave, enabling realistic, lab-based testing for defence communications using Heriot-Watt’s ground station.
Objectives
- Develop an IMU-based system to record real motion data from vehicles, such as cars, UAVs, or marine platforms.
- Design a software package that uses this motion data to control an antenna positioner and simulate real-world movement.
- Create machine learning models that can detect motion patterns and predict future movement to improve tracking accuracy.
- Test and evaluate satellite terminals under simulated motion using Heriot-Watt’s ground station to measure performance in realistic defence scenarios.
Description
This project focuses on improving how satellite communication systems for defence are tested and developed. Mobile platforms such as military vehicles, UAVs, and naval vessels require satellite links that remain stable under motion and in harsh environments, such as urban canyons, mountainous terrain, or contested electromagnetic environments with jamming or multipath interference. Testing these systems in the field can be expensive and hard to repeat. This PhD will develop a lab-based testing system that uses real motion data, recorded from vehicles using inertial sensors (IMUs), to control a robotic antenna positioner (hexapod). The system will also use machine learning to recognise patterns and predict future motion, allowing more advanced testing of how terminals behave. By using Heriot-Watt’s satellite ground station and hexapod equipment, the system will allow realistic and repeatable testing of terminals for defence use, helping to improve resilience, tracking, and performance before deployment.
Scientists to develop next-generation satellite user terminal for affordable global connectivity
UK technology consortium for Mobility and Autonomy Market User Terminal (MAMUT)
Applications
First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.
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.
Home rate fees and stipend are available for this position.
Research Themes
Aim
To develop ambient backscatter communication systems enhanced with AI-driven signal processing for secure, low-power data exchange in defence environments, enabling covert sensing and communication among autonomous platforms.
Objectives
- Design and optimise ambient backscatter transceivers for secure, energy-autonomous data exchange in contested RF environments.
- Develop machine learning algorithms for adaptive signal detection, classification, sensing and interference mitigation.
- Integrate secure communication protocols suitable for emissions-controlled and covert operational scenarios.
- Build and evaluate a prototype system demonstrating real-time performance and resilience in representative defence settings.
Description
This PhD project builds on previous pioneering work (LORAB) in ambient FM backscatter to enable secure, battery-less wireless communications in defence scenarios. These systems reuse existing RF signals (e.g. FM, cellular) for data modulation, producing signals that are several dB below the carrier and nearly undetectable, making them ideal for stealth operations. Their ultra-low power operation allows them to run on supercapacitors and energy harvested from the environment. The project will enhance CSS-based modulation and machine learning for improved range, security, and adaptability. With the current interest in integrated sensing and communications (ISAC)[JT1] , this work will also explore possible sensing functions of such signals. The goal is a portable, autonomous sensor tag capable of silent communication over hundreds of metres, using simple SDR-based receivers. This silent and thermally low-signature technology enables covert sensing of the environment or personnel without revealing the location or activity of the node. Its resilience will be tested under jamming and interception conditions, providing a low-profile solution for secure battlefield and intelligence applications.
Relevant references:
- Daskalakis, S. N., et al., “Ambient FM Backscatter: Towards Long-Range, Zero-Power IoT Communication,” Sensors, MDPI, 2022. https://daskalakispiros.com/files/daskalakispis_mdpi_2022.pdf
- Kimionis, J., Daskalakis, S. N., et al., “Backscatter Communications for Wireless Connectivity in IoT,” Nature Electronics, 2021. https://daskalakispiros.com/files/kimionis_nature_2021.pdf
- Daskalakis, S. N., et al., “Dual-Polarized Backscatter Sensor for RF and Wireless Applications,” IEEE Transactions on Microwave Theory and Techniques, 2018.
https://daskalakispiros.com/files/daskalakis_mtt_2018.pdf
- D. Galappaththige, et al., "Integrated Sensing and Backscatter Communication," in IEEE Wireless Communications Letters, vol. 12, no. 12, pp. 2043-2047, Dec. 2023.
Applications
First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.
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.
Home rate fees and stipend are available for this position.
Research Theme
Aim
Enabling anti-spoofing, anti-jamming receivers for current and future PNT systems
Objectives
- Extract unique radio frequency fingerprints (RFF) of GNSS satellites for spoofing resilient PNT receivers.
- Develop efficient multi-chain signal processing algorithms or AI tools to identify malicious parties in complex electromagnetic environment.
- Design autonomous null-steering RF frontend for anti-jamming PNT receivers.
- Showcase the security performance of developed PNT receiver in both lab and field testing, without compromising PNT accuracy.
Description
The increasingly dependent on GNSS-enabled positioning, navigation, and timing (PNT) in both military and civil applications demands more robust GNSS receivers which are resilient to both unintentional and intentional interference.
In collaboration with Spirent, which leads the global market for GNSS test equipment and emulation software (SimGNSS), we aim to develop both hardware- and software-enabled security approaches to PNT services. This includes 1) exploiting minute and unique radio frequency (RF) frontend differences as unclonable ID for genuine GNSS satellites to combat spoofing attack; 2) developing array-based autonomous beamforming designs, and associated efficient signal sensing and processing capabilities to reject potential malicious jamming and spoofing parties; and 3) integrate and prototyping security-enhanced GNSS receivers for practical demonstration.
It is importance to mention that these GNSS system challenges are recently documented in the ESA ITT (see the links below), highlighting the urgent needs in defence sectors.
- https://esastar-publication-ext.sso.esa.int/ESATenderActions/details/140407
- https://esastar-publication-ext.sso.esa.int/ESATenderActions/details/89700
- https://esastar-publication-ext.sso.esa.int/ESATenderActions/details/159629
Applications
First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.
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.
Home rate fees and stipend are available for this position.
Research Themes
Aim
Research on the algorithm development for automatic target detection, tracking and characterisation using event camera and spike neural networks.
Objectives
- Space Situation Awareness with Neuromorphic Systems
- Onboard processing with SNN
- Going beyond detection based on object shapes
- Object characterisation using micro-vibration
Description
Space Situational Awareness (SSA) has become a necessity in this congested space era. The fact is that finding moving objects, which may or may not be threats, in space is like finding a needle in a haystack. Neuromorphic imagers capture only changes in scene luminance and are quiescent when there is no change. This massively reduces data generation, but it requires a different processing chain to perform the task. Neuromorphic processing is power-efficient for embedded implementations of event data.
This project investigates neuromorphic processing for SSA applications using event-camera data. The aim is to use real data to characterise how objects of interest can be detected and characterised using shape, motion-track, and micro-vibration features. This can provide a step change over current capabilities, in which most processing occurs on the ground, and help avoid the bandwidth bottleneck of transmitting full imagery to Earth, and real-time detection may be possible.
Applications
First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.
A UK first class, or equivalent international degree on Electrical Engineering, Physics, Computer Science, Mathematics or similar.
Further information on English language requirements for EU/Overseas applicants.
Full funding available for this position