Imaging, Data and Communications

Objectives: 

1. Develop new adaptive data-driven sensor tasking and management strategies.
2. Explore combination of Bayesian signal processing and modern machine learning solutions.
3. Implement and evaluate developed techniques in simulated and real world scenarios.

Description:

Sensor networks, sensor fusion and management techniques address key challenges in intelligence, surveillance, target acquisition, and reconnaissance (ISTAR). Opportunities in adaptive data-driven sensor tasking and resource management include adaptive sensor placement, adaptive waveform design to reflect the target reflection characteristics and channel environments, and adaptive sensor selection. Although these problems have solutions in specific use cases, this project will consider scenarios with broader applications involving multiple heterogeneous sensors on single or multiple airborne autonomous collaborative platforms (ACPs).

The solutions developed in this should be robust to dynamic and congested environments, adverse weather conditions, and mutual sensor interference. A range of algorithmic and signal processing or machine learning technologies will be considered, as well as specific technical challenges. For example, this project could consider aspects related to wide area motion imaging (WAMI), position, navigation, and timing issues (PNT); robustness to adversarial attack; sensor fusion and tracking applications; use of machine learning and Monte Carlo methods. Probabilistic and Bayesian frameworks will be preferred to enable uncertainty quantification and management.

This project will be jointly supervised by Professor James Hopgood and Professor Mike Davies.

Research Theme 

Sensor Signal Processing

Industry Partner

Leonardo (as part of the Prosperity Partnership Smart Products Made Smarter) 

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.

UK 2:1. in computer science / computer systems or MSc in geographical information science.

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.

Further information and other funding options.

This PhD project aims to develop Physics-Informed Neural Networks (PINNs) for the prognosis and understanding of instabilities in zero-carbon reactive flows, with a particular focus on hydrogen and/or ammonia combustion. The project will integrate advanced machine learning techniques with laser imaging to create predictive, data-efficient models capable of capturing the complex, multi-scale dynamics of reacting flows. This project is under the Royal Society International Exchange Programme in collaboration with Shanghai Jiao Tong University.

Imaging methods such as absorption or emission tomography provide rich, high-resolution spatio-temporal data on flow characteristics. These measurements will be combined with governing physical laws embedded within PINN frameworks to infer hidden flow states, identify instability precursors, and forecast the onset and evolution of reactive-flow instabilities.

The research will involve developing tailored PINN architectures for reactive flows, designing strategies to assimilate experimental laser-imaging data, and validating models against laboratory-scale combustion experiments. Emphasis will be placed on uncertainty quantification, robustness to sparse and noisy data, and real-time or near-real-time predictive capability.

The project is highly interdisciplinary, spanning laser imaging, applied mathematics, and machine learning, and is suited to candidates with strong interests in both imaging and data-driven methods.

Primary objectives:

1. Develop PINN frameworks tailored to zero-carbon reactive-flow systems.
2. Integrate imaging diagnostics with PINNs for model training.
3. Identify and characterise precursors to instabilities from spatio-temporal flow fields.
4. Create predictive models for early-warning prognosis of instability.
5. Validate PINN predictions against controlled laboratory combustion experiments.
6. Investigate uncertainty quantification and robustness.

Required skills: 

1. Matlab/Python/Tensor-flow coding (mandatory)

2. Machine learning algorithms and coding (mandatory)

3. Tomography and inverse problems

4. Flow-field mechanics and dynamics

5. Optical/electronic experimental skills.

Please note that this advert will close as soon as a suitable candidate is found.

Applicants should have an Undergraduate degree in Electronic and Computer Science or Mechanical Engineering, possibly supported by an MSc Degree

Please also refer to the University’s English language requirements.

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

Further information and other funding options.

We are seeking an outstanding Doctoral Candidate (DC) for a fully funded studentship as part of the prestigious Horizon Europe MSCA Doctoral Network “Advanced Network Connectivity using Harmonious Optical and Radio Technologies (ANCHOR)” The studentship covers UK fees and provides a generous salary, consumables, and travel budget.

Extending high-speed wireless data transfer to the underwater environment has several benefits that include environmental protection, national security, mineral exploration, and monitoring of vital underwater infrastructure among others. This need has led to increased research in underwater optical wireless communication (UOWC). Recent studies in controlled in-lab testbeds have shown the possibility of a Gbps data rate. But achieving reliable, robust and resilient data transfer in the challenging underwater channel remains a challenge. This project will study UOWC and investigate approaches to link the underwater network to the much wider land and space communication networks. 

This project is also to study the existing radiative transfer theoretical framework of photon propagation in underwater to formulate a signalling/waveform design that is best suited to the UOWC channel. This will be followed by designing a new resilient waveform for UOWC and developing a computer-based simulation to evaluate its performance. Finally, the study will use existing UOWC channel emulator to demonstrate the feasibility of the new signalling/waveform.

The ideal candidate for this project would have first degree and MSc/MEng in physics, electronic and electrical engineering or a related discipline. A passion for experimental work and capability to develop/apply mathematical modelling techniques will be particularly important for this project. 

Expected start date: June 2026 or shortly thereafter.

Undergraduate and postgraduate degrees in Physics, Electronic and Electrical Engineering or related discipline.

Tuition fees + stipend are available for Home and International students. 

To qualify for the funding, the applicant must not have resided or carried out their main activity (work, studies, etc.) in the UK for more than 12 months in the past 36 months.

Further information and other funding options.

Research Theme

Multi-Agent Systems and Data Intelligence

This project is also supervised by Dr Sasa Radomirovic.

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.

Research Themes

Autonomous Sensing Platforms

Sensor Signal Processing

Aim

Optimise PNT as a Service using satellite signals of opportunity (SoOP) across LEO/MEO/GEO.

Objectives

1. Establish a testbed that will enable PNT services using satellite SoOP.
2. Benchmark the performance of PNT services using satellite SoOP against GNSS-based systems.
3. Optimise data management of a the system for performance and robustness
4. Develop AI-driven models for the selection of the optimum SoOP to be used in a PNT as a Service scenario

Description

GNSS-enabled positioning, navigation, and timing (PNT) is under increasing threats from interference and spoofing. 

Satcom systems offer signals of opportunity (SoOP) that can be used to deliver PNT services. They come from an abundant number of satellites across LEO/MEO/GEO, each offering distinct advantages in terms of signal strength or coverage. SoOPs are also available across a wide range of frequencies (from C-band to Ka-band) and multiple angular directions, thus making SoOPs a highly reliable option of pervasive PNT services.

This project will access a UK-wide network of monitoring and reference stations publishing SoOPs across C-, Ku- and Ka-bands. Data from this network will be used in conjunction with signals captured at a user terminal to deliver and benchmark PNT. The data management aspects of such a system will be investigated to optimise performance. AI-driven algorithms for selecting optimum usage of the proposed system will be developed and evaluated.

Industry Partner

This project will be in collaboration with PNTaas

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.

https://space.site.hw.ac.uk/ 

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.

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

Further information and other funding options.

Research Theme

Sensor Signal Processing

Aim

To advance generative AI technology for computational imaging by developing novel neural network architectures that combine the explainability, modularity and flexibility of MCMC-based Bayesian imaging methods with the accuracy and scalability of deep learning techniques.

Objectives

1. Develop new neural network architectures and supervised training strategies tailored for physics-informed generative image reconstruction and uncertainty quantification, with a focus on improving accuracy, scalability to very large problems (e.g., images of size 1024x1024 pixels or larger), explainability (e.g., with layers/modules that map clearly to instrumental models, data fidelity models and regularisation models) and modularity (e.g., that allow modifying or adjusting instrumental models and noise models during inference, without need for retraining).
2. Leverage the proposed architectures and the industrial partner’s expertise to co-develop novel Bayesian imaging solutions for a flagship application of interest to the partner.
3. Develop self-supervised training strategies to fine-tune the proposed architectures directly from measurement data, bypassing the need for ground truth data.

Description

Modern computational imaging method rely increasingly on deep generative models to address challenging inverse problems. A notable example is the LATINO-PRO sampler, which combines a large-scale Stable Diffusion XL image prior trained on over five billion image–text pairs with a Bayesian inversion framework tailored to generative AI, achieving state-of-the-art performance on difficult tasks such as x16 image super-resolution. This project aims to develop novel neural network architectures specialised for generative computational imaging. A key novelty is that the proposed architectures will be derived from modern MCMC samplers such as LATINO and from recent work on unfolding, training and distilling of MCMC algorithms into physics-informed generative neural networks. The resulting models are expected to be extremely fast while preserving the structure, interpretability, and uncertainty quantification capabilities of iterative Bayesian inference. This enables flexible inference, including runtime specification of sensing models and their automatic calibration via empirical Bayesian techniques. The goal is to deliver unprecedented image reconstruction accuracy while addressing critical requirements for quantitative imaging in security and defense, notably trustworthiness and explainability. We will consider applications to thermal, multispectral, or low-photon imaging, with possible extensions to dynamic imaging and video reconstruction. For scenarios where reliable ground-truth data are unavailable for training, we will investigate pre-training strategies using public or synthetic datasets, followed by self-supervised fine-tuning directly on sensor data.

The proposed project builds on the recent paper “Learning few-step posterior samplers by unfolding and distillation of diffusion models” https://arxiv.org/abs/2507.02686, which applies deep unfolding to the LATINO sampler https://arxiv.org/abs/2503.12615, delivering remarkably accurate posterior samples in as little as 8 neural function evaluations.

A UK first-class honors degree (or its international equivalent) in computational mathematics, computer science, electronic engineering, or a closely related discipline. This project also requires strong programming skills and experience with machine learning frameworks (e.g., PyTorch).

Further information on English language requirements for EU/Overseas applicants.

Full funding is available for this position.

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). 

Further information and other funding options.

Research Theme

Multi-Agent systems and Data Intelligence

Aim

Develop a framework to integrate and enhance multi-agent sensor data with prior geospatial intelligence to provide robust situational awareness via personalised (AR/VR/Speech UI) and overview (maps, data feeds) interfaces.

Objectives

1. Develop a decentralised edge computing architecture for geospatial data processing, able to accommodate rapid updates (e.g. sensors), for delivering dynamic situational awareness.
2. Investigate personalised HCI for different operator roles (e.g. VR/AR interfaces; speech-first hands-free; tangible; egocentric vs allocentric frames of reference).
3. Assess role-specific HCI adaptations via AI Agents (i.e. the ability for any user to use AI to design their own interface to the system)
4. Integrate and examine AI techniques for data validation and correction

Description

This research seeks to develop a decentralised edge computing solution to enhance situational awareness in multi-agent systems by integrating live sensor data with geospatial intelligence and real-time modelling.

Remote sensors deployed on robots and in the environment—capturing location, video, audio, and IMU data—will be fused with existing datasets such as LiDAR point clouds, digital surface models to provide personalized situational awareness.

A modular, extensible API will support analyses such as real-time visibility modelling for covert or signal-optimized route planning. AI-driven natural language queries will allow users to interact with the system.

Interfaces will include AR, tablets, and hands-free operation via speech recognition, augmented with LLM-based semantic processing and spatial audio. A key research focus will be integrating AI agents to allow end users—without programming skills—to easily design custom interfaces suited to their needs at any time.

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.

UK 2:1. in computer science / computer systems or MSc in geographical information science.

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.