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Coordination and Cooperation in Adversarial Engagements (EPSRC CDT in Distributed Algorithms)


Project Description

This studentship has been developed by the University of Liverpool and STFC’s Hartree centre in partnership with MBDA.

This PhD will develop methods to provide tactical guidance and decision making for future air defence systems. The aim will be to provide assistance to an operator that is robust, timely and that optimises the defensive response across all available assets; including platforms/vehicles, countermeasures, and interceptors.

Existing air defence systems are often optimised to provide protection against individual adversaries. Future systems are likely to require a greater level of adaptability and to provide the same degree of protection against multiple adversaries in complex, time-critical engagements. The ability to adapt to changes in tactics in real-time is a challenging computational task, and it is further complicated by constraints on the available countermeasures and other resources.

The key challenges in this work are in reducing the latency of the tactical responses (improving computational speed) and providing an indication of the reasoning behind the tactical guidance. The interpretability of the system’s outputs is critical to the understanding of the system and it is likely to be a major factor in the acceptance of such techniques. It will therefore require an appreciation of high-performance computing methods, and logical reasoning with incomplete data. The industrial, non-academic partner (MBDA) will help to define appropriate scenarios and will inform the choice of constraints and system capabilities.

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/

For informal enquires please contact Prof Jason Ralph or

Funding Notes

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).

References

Supervisors:

Professor Vassil Alexandrov - STFC Hartree Centre

I’ve worked in high performance computing (HPC), data and computational science for a long time, with a fulfilling career spanning 18 years and 5 countries! I’ve also published over 130 papers in journals and at international conferences and workshops. I’m excited to be a supervisor so that I can pass on my knowledge and experience to the next generation of young people who will develop research projects in exciting areas of HPC and data science.

During my career, I have supervised 31 PhD students to successful completion of their PhD studies across a variety of computational themes and areas, and been a Programme Director of 3 MSc programmes. I am a member of the Editorial Board of the Journal of Computational Science (JOCS) and Editor of Mathematics and Computers in Simulation journal.
Beginning in Russia, I achieved an MSc degree in Applied Mathematics from Moscow State University, followed by a PhD degree in Parallel Computing from Bulgarian Academy of Sciences. I have also previously held positions at the University of Liverpool, UK, the University of Reading, UK, and Monterrey Institute of Technology and Higher Education (ITESM), Mexico.

In 2019 I was appointed as Chief Science Officer at the Science and Technology Facilities Council (STFC) Hartree Centre in the UK. Previously I was an ICREA Research Professor in Computational Science at Barcelona Supercomputing Centre, Spain.

I have a lot of experience in stochastic modelling, Monte Carlo methods and algorithms, parallel algorithms and scalable algorithms for extreme scale computing, e.g. for large-scale systems and applications. My long-term expertise in Monte Carlo means I am particularly interested in seeing how we can further speed up these simulations.

Currently, mathematics-led innovation is clearly indispensable in advancing key scientific areas, as well as powering methods and algorithms enabling to discover global properties of data.

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