Electronics and Electrical Engineering

Research Theme

Sensor Signal Processing

Aim

This EngD project will investigate the implementation of real-time signal processing algorithms within FPGA-GPU processing pipelines. 

Objectives

1. Investigate the suitability of GPGPU architectures for streaming signal processing workloads by implementing and evaluating adaptive radar processing algorithms. This should include assessments on throughput, latency, and energy efficiency compared to multi-core CPU implementations.

2. Design, implement, and evaluate a software product line (SPL) architecture for signal processing applications. The developed architecture should support configurable processing chains and efficient exploitation of heterogeneous compute, while optimising for performance, maintainability, and portability.

3. Investigate how heterogeneous SPL architectures can be verified to meet the objectives and assurance requirements of DO-178C, identifying any architectural and verification practices necessary.

Description

As the benefits of Moore's Law continue to diminish, the computational demands of modern signal processing systems are increasingly outpacing the performance gains available from conventional CPU architectures. While multi-core processors have extended the life of CPU-based solutions, many real-time applications must now process larger volumes of data under increasingly demanding latency constraints.

In airborne sensing systems, large quantities of in-phase and quadrature (IQ) data are generated by FPGA-based processing chains. Traditionally, this data is processed on general-purpose CPUs; however, many signal processing algorithms are highly parallel in nature and may be better suited to Graphics Processing Units (GPUs). The success of GPUs in fields such as AI and scientific computing has shown their ability to deliver significant improvements in throughput and processing performance, raising the question of how applicable they are to real-time signal processing.

Early investigations have shown promising results. At NVIDIA GTC 2025, SAAB presented a GPU-accelerated signal processing chain implemented using CUDA, reporting approximately a tenfold improvement over an equivalent CPU implementation. In parallel, NVIDIA's Holoscan platform is enabling high-rate sensor data to be streamed directly from FPGA hardware to GPU for processing pipelines. While this opens up new possibilities for real-time processing, it also raises questions around performance predictability, system integration, verification and software assurance, all of which are important considerations for flight-qualified systems.

This EngD project will investigate the implementation of real-time signal processing algorithms within FPGA-GPU processing pipelines. Using NVIDIA Holoscan as the underlying framework, the research will evaluate the performance and latency characteristics of GPU-accelerated processing while exploring the verification and validation challenges associated with deploying such technology in airborne systems. The aim is to better understand both the benefits and the practical barriers to adopting GPUs within future real-time sensing platforms.

In-situ optical spectroscopy during Laser Powder Bed Fusion (LPBF) captures high-resolution signals encoding the physical state of the melt pool. However, this data presents a formidable statistical challenge: observations are strongly non-i.i.d., exhibiting spatiotemporal autocorrelation, directional asymmetry from moving laser paths, and complex thermal dynamics. Because labeled defect data is rarely available at scale, this project bypasses supervised approaches to build principled, physics-informed unsupervised learning frameworks. 

This project will build on the supervisors’ research in stochastic modelling, statistical learning, numerical analysis, and mathematically grounded anomaly detection. The research will focus on formulating flexible, label-free statistical machine learning for real-time anomaly detection and defect classification. Depending on candidate interest and project evolution, potential methodological avenues include combining spatial modelling with non-parametric hypothesis testing to separate systematic process variation from distributional shifts. To account for complex spatiotemporal dependencies, you will investigate robust calibration schemes (such as block-permutation or wild-bootstrap methods), while exploring techniques ranging from sparse matrix decomposition to geometric clustering and manifold learning to isolate and classify physical anomaly regimes like porosity, lack of fusion, or keyhole collapse. 

Bridging spatial statistics, rough path theory, functional data analysis, and advanced process engineering, this project translates rigorous mathematical methodology into immediate industrial impact for metal additive manufacturing.

• a 1st Class undergraduate degree (or equivalent).
• 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.

Reconstructing 3D gas concentration and temperature profiles within dynamic, turbulent plumes from sparse optical measurements is a classic ill-posed inverse problem. Positioned at the intersection of radiative transfer, fluid dynamics, and statistical physics, this project aims to push the boundaries of computational imaging in extreme, harsh environments. 

The research will focus on developing forward models governed by coupled radiative and heat transfer PDEs, while exploring alternative, more informative measurement modalities to significantly boost spatial resolution and noise robustness under severe data scarcity. To address the chaotic nature of dynamic flows, you will construct Bayesian inference frameworks and random media models to parameterise chaos and achieve data-consistent uncertainty quantification. Furthermore, the project offers scope to integrate scientific machine learning, such as neural operators and diffusion models, to efficiently represent complex, swirling fluid structures alongside classical physics-based inversions. Bridging deep mathematical theory with real-world applications in aerospace propulsion, combustion efficiency, and environmental monitoring, this project involves direct collaboration with specialists in gas metrology and aerospace engineering to validate computational models against experimental data. 

It is ideal for ambitious candidates in Engineering, Applied Mathematics, Computational Physics, or Signal Processing with a strong foundation in differential equations, linear algebra, inverse problems, or statistical learning.

• a 1st Class undergraduate degree (or equivalent).
• 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 invite applications for a PhD focused on data-driven and digital solutions to accelerate the decarbonisation of electricity systems - in a way that yields benefits for the grid, end-users/communities, system resilience and beyond. The successful candidate will develop, test and validate methods that combine some of digital twins, AI/ML, optimisation, novel market mechanisms (e.g., peer-to-peer trading, flexibility markets) and enabling technologies (e.g., smart contracts) to coordinate distributed energy resources such as EVs, batteries, heat pumps and beyond. The project aims to bridge academic research and real-world implementation, delivering tools that enhance resilience, flexibility and affordability in smart, low‑carbon grids. The position sits within the Institute for Energy Systems (Electronics & Electrical Engineering) and builds on our recent work on: - community energy and local energy solutions that support Just Energy Transitions - demand-side response, energy flexibility assessment and smart coordination (via optimisation and AI decision-making) - smart contracts and transactive energy in local markets - virtual power plants (VPPs) of EVs and residential batteries, including extreme-weather resilience.

If a suitable candidate is found, this position may close earlier than the closing date.

The applicants should hold a first degree in Engineering, Physics, Informatics or similar.

Further information and other funding options.