This studentship has been developed by the University of Liverpool in partnership with IBM.
This project is focused on showing that raw aggregated capability can outperform carefully constructed co-ordination. More specifically, while dedicated high-performance computing resources provide a carefully constructed homogeneous environment that can make best use of the available hardware, there are settings where the availability of vast quantities of computational hardware should more than make up for the disparate connectivity and capability of the hardware.
Recent collaboration between the University of Liverpool and IBM Research has developed numerical Bayesian techniques (Sequential Monte Carlo samplers) that exploit homogeneous super-computing hardware to outperform algorithms that are, by default, configured to make use of a single processing core (e.g., Markov chain Monte Carlo). These techniques pave the way for a generic solution to the problem of performing algorithmic teamwork in the context of data science.
This PhD will investigate whether it is possible to adapt the pre-defined divide-and-conquer algorithm at the heart of the aforementioned numerical Bayesian techniques to adapt to and operate effectively within an uncertain computational environment. This will involve the development of fast distributed algorithms to identify and instantiate a divide-and-conquer architecture that is near-optimal given the available resources.
The aim of the project is to develop the infrastructure that makes it possible for vast heterogeneous compute resources (e.g., based on a disparate mix of GPUs, desktop PCs and android devices) to operate effectively in a team. If successful, the aim is to use spare computer infrastructure available globally (e.g., via a system akin to [email protected]
) to deploy algorithms based on teamwork to answer a fundamental societal question to be identified by the student.
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/