Imaging, Data and Communications
Research Theme
Aim
This project consists of developing single and multi-object detection (and ultimately tracking) algorithms from neuromorphic camera data.
Objectives
- Identification of tractable (front-end) network architectures able to capture spatio-temporal patterns in spiking data
- Discrimination of spatio-temporal signatures of objects of interest from dynamic clutter.
- Efficient fusion of neuromorphic and frame-based imaging sensors for real time tracking of fast-moving objects.
Description
Neuromorphic cameras have enabled low-cost and fast imaging by only recording asynchronously intensity changes. However, efficient processing of the massive streams of polarised events remains a challenge for real-time computer vision tasks such as object detection. Moreover, platform motion also generates dynamic clutter events that can impede object detection and tracking. In this project, we will investigate artificial architectures trained to encode event streams into low temporal resolution frames while also capturing high-resolution spatio-temporal features (e.g., periodicity). Such features can then be used to improve detection and classification performance (e.g., for UAV detection) or platform motion estimation (for enhanced simultaneous localisation and mapping). Finally we will investigate how neuromorphic cameras, operating at different spatial and temporal resolutions can be fused with traditional frame-based cameras (multimodal fusion).
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 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)
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.
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
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.
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
Research Theme
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. Further information on English language requirements for EU/Overseas applicants.
Full funding is available for this position.
Research Theme
Aim
To develop a defence-ready, low-cost alternative to AI post-training that lets air-gapped AI systems rapidly adapt in the wild
Objectives
- Build a local “module bank” of lightweight adapters (LoRAs) covering defence relevant sensors and tasks.
- Train a learned router that picks and combines the right modules for new scenes, using crossmodal alignment and simple ondevice self-supervision
- Prove the system on air‑gapped edge hardware, measuring speed, accuracy, and reliability under distribution shift
Description
We will develop a low-cost alternative to AI post-training that relies on exploiting a local bank of neural modules, implemented with parameter-efficient adapters (e.g., LoRA) and related lightweight components, for deployment on defence and security platforms. This will allow an AI in the wild—operating in air‑gapped or contested settings—when exposed to a new scenario (e.g. an unidentified object, a change of scene) to rapidly adapt to what it senses using the most appropriate modules, guided by a learned router that selects and composes modules and supports lightweight on‑device self‑supervised retraining to track distribution shift. We will optimise this module bank using both generative and discriminative tasks across defence‑relevant modalities (e.g. natural images, EO/IR, SAR/sonar, RF, audio) and develop algorithms that are able to express a new task/modality in terms of these tasks/modalities to identify the most appropriate modules via cross‑modal alignment into a shared task/sensor embedding space.
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 devise novel, explainable, and robust speech deepfake detection methods that go beyond current and next-generation techniques.
Objectives
- Understand the vulnerabilities of State-of-the-Art deepfake detectors when faced with the latest methods for speech synthesis.
- Construct purposely-designed attacks and simulations of beyond-next-generation synthetic speech to further explore these vulnerabilities.
- Devise novel approaches to constructing a deepfake detector that address the identified vulnerabilities, and go beyond binary fake vs. bonafide decisions by providing measures along multiple dimensions.
- Explore the accuracy, utility, and usability of the detector and its explanations, when applied to use-cases in the field.
Description
Digitally created or manipulated synthetic speech of remarkably high naturalness is a reality. Beneficial uses include text-to-speech for people who cannot speak, and privacy-preserving identity protection. But speech deepfakes enable deception, from scam calls to large-scale election interference on social media. Current automatic detection methods learn to rely on specific low-level artefacts in the synthetic speech used to train them, making them vulnerable to newly-emerging deepfakes, whilst incorrectly classifying some natural speech as fake.
In this project, you will rethink deepfake detection. For example, can we take inspiration from human judgments? Humans do not need to have previously heard a specific type of deepfake, presumably because we have a strong internal model of natural speech. Can we decompose “naturalness” into useful dimensions that enable explainable deepfake detection, whether fully-automated or human-AI collaboration (e.g., tools for speech forensic scientists). Better methods of speech evaluation will also advance research in speech generation for beneficial use-cases.
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 project combines elements of audio engineering, linguistics, speech science, forensics, and AI, so we are looking for applicants who have a background in one or more of those areas and the aptitude to acquire the necessary skills in the other areas during the PhD.
Note: we do not require an undergraduate degree in a STEM subject but would, for example, consider an applicant with an undergraduate degree in linguistics plus a Masters in Speech & Language Processing.
Full funding is available for this position.