This studentship has been developed by the University of Liverpool and STFC’s Hartree centre in partnership with DSTL.
This PhD will develop techniques to schedule the use of distributed computational resources to maximise the utility of the information generated. This contrasts with current streaming Big Data middleware (e.g., Apache’s Storm or IBM’s Infosphere Streams), which schedules the use of resources to maximise the rate at which data is processed (and does not consider how useful the data will be).
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 reasoning about what information might exist (and where) faster than calculating that information. The focus of the PhD will therefore centre on calculating statistical emulators that can predict the utility of information and scheduling distributed computational resources using those emulators. Use cases and metrics for utility will be co-defined with the non-academic partner, Dstl.
This project is part of the EPSRC Funded CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science. https://www.liverpool.ac.uk/research/research-at-liverpool/research-themes/digital/cdt-distributed-algorithms/
The University of Liverpool is working in partnership with the STFC Hartree Centre and other industrial 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 data science, be it in academia or industry.
Every project within the centre is offered in collaboration with an Industrial partner who as well as providing co-supervision will also offer the unique opportunity for students to access state of the art computing platforms, work on real world problems, benchmarking and data. Our graduates will gain unparalleled experiences working across academic disciplines in highly sought-after topic areas, answering industry need.
As well as learning from academic and industrial world leaders, the centre has a dedicated programme of interdisciplinary research training including the opportunity to undertake modules at the global pinnacle of Data science teaching. A large number of events and training sessions are undertaken as a cohort of PhD students, allowing you to build personal and professional relationships that we hope will lead to research collaboration either now or in your future.
The learning nurtured at this centre will be based upon anticipation of the hardware recourses arriving on desks of students after they graduate, rather than the hardware available today.
To apply for this Studentship please submit an application for an Electrical Engineering PhD via our online 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 see our website https://www.liverpool.ac.uk/research/research-at-liverpool/research-themes/digital/cdt-distributed-algorithms/
Open to EU/UK applicants
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 (£15,009 per annum, 2019/20 rate).