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 theme will consider scenarios with broader applications involving multiple heterogeneous sensors on single or multiple cooperative autonomous airborne platforms.

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, projects in this theme will 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 kernel and Monte Carlo methods; outlier-robust (and other metrics) messages in belief propagation algorithms; and scheduling in large dynamic networks. Probabilistic and Bayesian frameworks will be preferred to enable uncertainty quantification and management.

The techniques, solutions, and challenges proposed in this theme has applications across a range of defence and civilian applications, including search and rescue, law enforcement, and remote sensing.

This project will be jointly supervised by:

Prof James Hopgood, James.Hopgood@ed.ac.uk

Prof Mike Davies, Mike.Davies@ed.ac.uk

Smart Products Made Smarter

The PhD project forms part of a larger Prosperity Partnership Programme, Smart Products Made Smarter, a collaboration with Heriot-Watt University, University of Edinburgh and Leonardo. 

We are pleased to invite applications for a PhD studentship to work as part of a leading team of experts. This studentship will be supported by an enhanced stipend of £20,716 per year over 3.5 years.

This grant, sponsored by the EPSRC, is a collaboration between academia and Leonardo. There are currently PhD opportunities available to work on diverse topics as part of this collaborative team. The work will involve strong links with industry.

The research addresses a broad range of challenges. These challenges exemplify future product lifecycle management from smart concept, design, development and manufacture to enhanced end-user capability, united by a common digital thread to enable smarter products to be made smarter. Each challenge area has clearly identified initial research themes and associated research challenges to be addressed and these are indicated below:

Challenge 1 (C1) the Making challenge: To create new hybrid manufacturing processes, that combine multiple Additive Manufacturing (AM) process with precision machining and coating processes to create components that disrupt the traditional functional trade-offs of Size, Weight and Power (SWaP) through techniques such as varying the material properties within a part and harnessing the digital production of optical components.

Challenge 2 (C2) the Manipulation challenge: To create new handling processes that fully exploit the digital data flows which define custom components whose shape and functionality is tailored to production by dexterous, highly adaptable robots that are programmed dynamically.

Challenge 3 (C3) the Computation challenge: To create new signal processing & machine learning methodologies that enable intelligent, digital & connected sensor products while mitigating the data deluge from the multiple sensors produced by Leonardo operating across the EM spectrum.

The themes represent areas that could form the basis of your PhD. These PhD positions offer great flexibility and we welcome the opportunity to explore other ideas & themes

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