This studentship has been developed by the University of Liverpool in partnership with Denbridge Marine.
This project is focused on using large historic records of sensor data from port security systems to identify the types of ship present and accurately reflect the sub-types present, i.e., perform classification and clustering in a dynamic context.
Ports deploy radars, cameras and other sensors to maintain awareness of the ships in and around the port. While ships can deliberately communicate their type (e.g., fishing boat, ferry, yacht, tug), some do not. Even those that do fail to fully characterise all the information that might be pertinent to the port (e.g., the type of fishing boat etc). A current project at the University of Liverpool is investigating how to use geometric data structures to create an abstraction of such historic data. Using this abstraction, the project is then performing behavioural classification and clustering. Unfortunately, the current processing chain requires the iterative sequential reprocessing of the data (i.e., iteratively analysing the data from start to finish) and assumes a feed-forward model (i.e., such that the pre-processing that generates the abstraction cannot be refined to improve the classification and clustering).
This project will seek to develop algorithmic approaches that can replace the current sequential and feed-forward processes with alternatives that are better suited to fully exploiting implementation on emerging many cored processing architectures. The project will involve extensive interactions with Denbridge Marine, a small company based near to Liverpool which delivers port security systems to customers around the globe. Denbridge Marine will help provide access to pertinent data, ensure that the research is well focused on challenges that their customers pose and provide a route for the techniques developed to be applied in the context of meeting those customers’ needs.
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/