This PhD project is part of the CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science.
The University of Liverpool’s Centre for Doctoral Training in Distributed Algorithms (DA CDT) 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.
The successful PhD student will be co-supervised and work alongside our external partner Defence Science and Technology Laboratory (Dstl).
PhD Project Overview: Mature techniques exist to schedule the use of a disparate mix of sensors (e.g., cameras on drones or sensor arrays distributed across a large geographic area) to maximise the utility of the information that can be derived from the sensed data. However, only a modest quantity of research has investigated how to schedule what subset of a disparate mix of distributed processing is applied. For example, given a network of sensors receiving data, future middleware needs to reason about the long-term impact of communicating data to a central server to perform an accurate, but time consuming, analysis, rather than using processing in close proximity to the sensor to provide a timely, but less accurate, output.
The key challenge in this context is ensuring we can reason about what we might calculate elsewhere and in the future without actually performing the calculation itself: we need statistical models for the future distributed processing tasks just as sensor management uses models for future sensing tasks.
The focus of the PhD will therefore centre on developing statistical emulators that can predict how computation will process data and generate information. Once those emulators exist, the focus will be on using the emulators to schedule distributed computational resources. Use cases and metrics for utility will be co-defined with the non-academic partner, Dstl.
Students are based at the University of Liverpool and part of the DA CDT and Signal Processing research community. Every PhD is part of a larger research group which is an incredibly social and creative group working together solving tough research problems. Students have 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 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.
This project is due to commence 1 October 2021 (Covid-19 Working Practices available).
For general application process queries contact firstname.lastname@example.org
To apply for this Studentship please 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 studentship title and supervisor details when prompted. Should you wish to apply for more than one project, please provide a ranked list of those you are interested in.
For a full list of the entry criteria and a recruitment timeline (including interview dates etc), Please visit the DA CDT website.
Open to EU/UK applicants
This PhD project is a funded, 4 year studentship and will provide UK tuition fees and a UKRI Doctoral Stipend (@ the applicable annual rates).