Electronics and Electrical Engineering

Professor Wasiu O Popoola

Professor Majid Safari

w.popoola@ed.ac.uk

Multi-Agent Systems and Data Intelligence

Security certification of AI-based System in Multi-Agent Context

All our international places for 2026 entry are now filled and we are no longer accepting applications from overseas students

Research Theme

Aim

Develop a framework for the certification of AI-based systems for safety and security.

Objectives

1. Formalise existing methods for assessing AI-based systems to identify building blocks for the framework.2. Develop analytic methods combining compositional reasoning and machine learning pipelines to evaluate AI-based systems.3. Apply the approach to the context of remote attestation in multi-agent systems.

Description

This project targets the fundamental question of the certification of AI-based data-systems for security and safety. This has been identified as a crucial open question by the 2023 AI Safety Summit and its recent follow-ups. Challenges posed by novel AI models require to re-think the analysis and certification of systems. By getting inspiration from the analysis of security and safety of software systems, this project combines knowledge and practice of machine learning and AI, and formal approaches of compositional reasoning for verification. The project is to conduct experiments targeting the problem of forming coalitions of agents in a multi-agent system where some of the agents do not belong or have been compromised. A solution requires remote attestation, whereby an agent proves that they are uncompromised (aside from authenticating themselves). The project contributes to Dstl’s secure by design adoption by providing a means to construct and evaluate secure AI-based systems.

Bengio et al. 2025. The Singapore Consensus on Global AI Safety Research Priorities. https://doi.org/10.48550/arXiv.2506.20702 Dalrymple et al. 2024. Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems. https://doi.org/10.48550/arXiv.2405.06624 Dstl. 2025. Secure by Design Problem Book. https://www.gov.uk/government/publications/secure-by-design-problem-book

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 fee rate and stipend available for this position.

29 May, 2026

Off

Prof Sasa Radomirovic

Prof Michael Lones

spads@ed.ac.uk

Further information and other funding options.

Machine Learning in Dynamical Systems for Sensor Signal Processing

Dynamical system models have been the main pillar of conventional model-based approaches in control, signal processing and sensor fusion: Sensor signal processing and inference algorithms for applications such as multi-object detection and tracking, robotic simultaneous localisation and tracking (SLAM) and calibration of autonomous networked sensors are designed by combining the known physics and stochastic elements into dynamic system models. Model inaccuracies can be mitigated to achieve significant performance gains in inference and decision-making by leveraging data and model size, following the recent advances in machine learning. However, learning from data in dynamical system models to jointly address the epistemic and aleatoric uncertainties involved remains a challenge stemming from a range of factors such as noisy data and labelling, inhomogeneous sampling, model complexity and the intractability of posterior inference.

This PhD project aims to conduct underpinning research to address problems involving the learning of hierarchical, time-varying multi-dimensional state space models for dynamic objects/phenomena, noisy measurements made in complex backgrounds and/or in the presence of calibration errors.

The incumbent will have the opportunity to steer the direction of the research in consideration of the impact on engineering problems, including learning models for complex backgrounds in radar detection, learning of birth and trajectory models to improve detection and tracking, or semi-supervised/unsupervised training of sensor data classifiers.

The application documents should provide sufficient information and evidence regarding the applicant’s background and goals, and include:

A Personal Statement
CV
Official certificates and transcripts (and English language translations, if applicable)
English Language certificate (if applicable, for more information, please see here: https://study.ed.ac.uk/programmes/postgraduate-research/947-engineering)
2 Recommendation Letters
Research proposal (optional for candidates who are not self-funded or applying for scholarships. Must be related to the project description)

Dr M Uney is an experienced scientist with interests in signal processing, machine learning, probabilistic models and Bayesian computations in sensor fusion and signal & information processing.

Prof Mike Davies holds the Jeffrey Collins Chair in Signal and Image Processing at the University of Edinburgh. His research interests are in machine imaging, data-driven computational sensing and imaging, and sensor and information fusion.

This position is open to UK/EU nationals and international applicants, and offers an annual stipend of £21,935 (as of 2024-25 fiscal year, subject to revision) for a duration of 3.5 years with £5000 research expense funds for the duration of the study.

UK nationals and other eligible applicants might choose to align this studentship with the Sensing, Processing and AI for Defence and Security Centre for Doctoral Training (SPADS CDT) at the University of Edinburgh’s School of Engineering, and benefit from an enhanced stipend (subject to CDT administration approval and security clearance, see the eligibility criteria in the link) upon acceptance. Eligible applicants should state their interest in their Personal Statement.

Minimum criteria:

- A 2:1 Master’s Degree in engineering, computer science, statistics, physics or in an appropriate subject or a related discipline.

- The University’s English language requirements

Preferable criteria:

- A 1st class Undergraduate Degree in engineering, computer science, statistics, physics or in an appropriate subject or a related discipline.

- A 1st class Master’s Degree in engineering, computer science, statistics, physics or in an appropriate subject or a related discipline.

- Experience in scholarly writing.

Tuition fees + stipend are available for Home/EU and International students

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere

15 Mar, 2026

Professor Michael Davies

Dr Murat Uney

M.Uney@ed.ac.uk

Further information and other funding options.

Robust Data-Fusion for Drone Surveillance Systems Using Cross-Domain AI

Drone-based Earth observation, surveillance, and goods delivery solutions are among the recently enabled technologies that heavily rely on the reliability of on-board sensors and the integrated processing of collected data. Such sensor systems can be unreliable or occasionally unavailable in certain modalities, such as cameras and LiDAR in foggy, cloudy, or dusty environments, and radar in radio-frequency congested or denied environments.The aim here is to conduct low-shot training of cross-domain AI models in order to: (1) improve the overall reliability of data analysis when some modalities are absent or noise-contaminated in harsh situations, and (2) enhance the overall accuracy of existing models.We aim to extend the scientific understanding and basic technology solutions for drone-based sensor platforms operating in urban environments. These solutions have various applications in security, defence, climate change monitoring, and humanitarian domains, such as disaster surveillance, search and rescue, and urban planning surveillance.

Please note that this advert might close sooner once a suitable candidate is found. Therefore early applications are advised.

Drone surveillance has various potential applications of interest to relevant industry. While such flying objects are equipped with multiple sensors, a reliable data-adaptive sensor fusion framework is required to accommodate erroneous measurements and provide the necessary precision and reliability for the task. This PhD project specifically aimed to address the following key research questions:“Accuracy gap”: How can data from different modalities, with varying accuracies, be combined to improve overall decision performance, such as detection, tracking, and classification accuracy?“Reliability gap”: How can the unreliability of certain modalities be identified and mitigated using limited data from other modalities?“Generalisation gap”: How robust is the solution to distribution shifts in the inputs, i.e., slight changes in the mode of operation such as day–night transitions?

a 1st Class undergraduate degree (or equivalent);
University’s English language requirements.

Fudning is available for home applicants only (UK+EU settled/pre-settled). For more information on this, please contact the project spervisor, Dr Mehrdad Yaghoobi (m.yaghoobi-vaighan@ed.ac.uk).

01 May, 2026

Dr Mehrdad Yaghoobi Vaighan

Professor Mike Davies

m.yaghoobi-vaighan@ed.ac.uk