Machine Learning for LDEW target recognition and line-of-sight stabilisation.

About the PhD Project

The project is to contribute to a major EPSRC research programme intended to develop generation after next technologies for applications in defence and security, and this project will be co-funded by QinetiQ.      

The project will be concerned with Machine Learning for LDEW target recognition and line-of-sight stabilisation.

The aim of this project is to apply machine learning techniques to identify air targets from high-frame rate (and low contrast) imagery that is representative of LDEW tracking systems. In this project the student would be expected to develop Machine Learning based algorithms to process fast-frame rate imagery and identify air-based objects to determine potential threats. There will be a requirement to do this in time scales that are relevant for the stabilisation the aim point of the LDEW tracking system, and it should be robust to confounding factors, such as smoke and visible countermeasures. This will involve using image processing techniques and appropriate processing of sequences of images of the same object to ascertain key identifiers and comparing these to known objects. This may include the use of 3D object models and/or large image databases.

The plan is to recruit a PhD candidate to undertake this project, who will be aligned to the EPSRC Energy Transfer Technologies Skills and Training (S&T) Hub. The main aim of the S&T Hub is to train the next generation of leaders in energy transfer technologies relevant for defence and other related applications. The Hub is supported by MoD, Dstl, and UK companies working in the defence and security sector.

Each student funded by the Hub will have an industrial partner and opportunities to work with, and train alongside, experts from industry. The Hub offers individuals training for both an academic and an industrial career path.

The student will be primarily based at the School of Electrical Engineering, Electronics & Computer Science, University of Liverpool, and will benefit from cohort-based training and the network of PhD students across a number of UK institutions. The Skills and Training Hub will run online and face-to-face activities to facilitate cohort building and group learning exercises throughout the PhD programme. The duration of the PhD is 4 years, and the start date is 1st January 2025.

https://www.liverpool.ac.uk/energy-transfer-skills-training-hub

The industrial partner, QinetiQ, is an integrated global defence and security company focused on mission-led innovation. QinetiQ employ more than 8,500 highly skilled individuals, committed to creating new ways of protecting what matters most; testing technologies, systems, and processes to make sure they meet operational needs; and enabling customers to deploy new and enhanced capabilities with the assurance they will deliver the performance required.

Eligibility

PhD Candidates must hold a minimum of an upper Second-Class UK Honours degree, or international equivalent, in a relevant science or engineering discipline. Candidates must be UK Nationals and be willing to apply for and able to obtain Baseline Personnel Security Standard (BPSS) clearance.

Before you apply

We strongly recommend that you contact the supervisor(s) for this project before you apply.

Equality, diversity and inclusion

The S&T Hub is committed to providing an inclusive environment, in which students can thrive. The Hub particularly encourages applications from women, disabled and Black, Asian and Minority Ethnic candidates, who are currently under-represented in the sector. We can also consider part-time PhD students. We encourage talented individuals from various backgrounds, with either an UG or MSc in a numerate subject and people with ambition and an interest in making a difference to apply.