EPSRC Fully Funded Project: Distributed Machine Learning for Automatic Annotation and Analyses of Vast Distributed Image Archives (EPSRC CDT in Distributed Algorithms)

Description

This PhD project is part of the EPSRC CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science.

The University of Liverpool is working in partnership with the STFC Hartree Centre and 20+ external partners from the manufacturing, defence and security sectors to provide a 4-year innovative PhD training course that will equip over 60 students with: the essential skills needed to become future leaders in distributed algorithms; the technical and professional networks needed to launch a career in next generation data science and future computing; and the confidence to make a positive difference in society, the economy and beyond.

This PhD project is developed in partnership with Collins Aerospace – a global company and leader in technologically advanced and intelligent solutions for the global aerospace and defence industry. The successful applicant will benefit from working on real-world, complex data challenges with support from academic and industrial supervisors, collaborative teamwork and placement opportunities. Due to the nature of the work, only UK Nationals are eligible.

This PhD project is to tackle the challenge of efficiently analysing the content of vast volumes of high-resolution imagery with distributed machine learning techniques such as federated learning. The images are generated with emerging sensors and stored in locations that span the Earth. Were it possible to bring all the imagery to one central location, it would be possible to use centralised machine learning to auto-annotate the imagery and thereby generate a list of geo-temporally localised objects of each of many types (cars, trees, buildings, bridges etc). This list could then be processed in such a way that, for example, changes could be identified and/or similar objects to a query object localised. Since the images are individually large and typically also very large in number, communications bandwidth considerations mean that it is not practical to send all the imagery to one centralised location. The privacy regarding local datasets may render the transmission to a central location legally infeasible. What is needed is a mechanism whereby the (hypothetical) centralised processes can be emulated in a (real) distributed, asynchronous setting. This will require development of both the infrastructure to facilitate distributed storage and querying (via e.g., distributed spatial indexing, learning-enabled intelligent querying, etc) as well as development of distributed algorithms (via e.g., map-reduce, federated learning, etc) that can operate in such a distributed, asynchronous setting.

The project aims to create non-trivial data science and computing solutions that offer a distributed machine learning mechanism whereby the (hypothetical) centralised processes can be emulated in a (real) distributed setting. This will require development of both the infrastructure to facilitate distributed storage and querying (via e.g., distributed spatial indexing, learning-enabled intelligent querying, etc) as well as development of distributed algorithms (via e.g., map-reduce, federated learning, etc) that can operate in such a distributed, asynchronous setting.

Students are based at the University of Liverpool and part of the DA CDT and Signal Processing  research community. Every PhD has 2 academic supervisors and an industrial partner who provides co-supervision and placements and also offers DA CDT students the opportunity to work on real-world, complex data challenges. In addition, students attend cohort and individual technical training to gain unparalleled expertise, work across academic disciplines in highly sought-after topic areas and have the opportunity to attend UK and international conferences. A large number of events and training sessions are offered allowing DA CDT students to build personal and professional skills that will complement the technical training to give you the confidence and tools to make a difference now and in the future. And each student is offered additional funds for personal, research-related training and development opportunities.

This project commences 1 October 2021 (Covid-19 Working Practices available).

For informal technical enquires contact 

For general application process queries contact 

To apply follow the DA CDT Application Instructions. Submit an application for an Electrical Engineering PhD via the University of Liverpool’s online PhD application platform (https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/) and provide the project title and supervisor details when prompted. Should you wish to apply for more than one project, please provide a ranked list of those you're interested in.

For a full list of entry criteria and a recruitment timeline (including interview dates), please visit: https://www.liverpool.ac.uk/distributed-algorithms-cdt/apply/

Availability

Open to UK applicants

Funding information

Funded studentship

This project is a fully funded Studentship for 4 years in total and will provide UK/EU tuition fees and maintenance at the UKRI Doctoral Stipend rate (£15,285 per annum, 2020/21 rate).

Supervisors