Track Before Detect Bayes (EPSRC CDT in Distributed Algorithms)


Please note this opportunity is only available to UK students.

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

This project is concerned with developing efficient and adaptive object tracking methods based on the Track Before Detect (TBD) technique, adapted to allow prior knowledge to be incorporated into the processing chain and making use of modern Bayesian sampling techniques.

TBD is an established track processing method, which uses information from sub-threshold ‘weak’ detections to improve tracking performance for low contrast objects. The proposed project would look at two main aspect of the problem: the use of lower power/lower cost processors, and the inclusion of modern Bayesian sampling methods to allow the use of supplementary information sources (such as prior information, based on known terrain types and or known object characteristics). The aim would be to develop a scalable processing architecture that allows large numbers of objects to be tracked across a distributed set of processors.

The key challenges are in developing real time processing methods for distributed processors that can use low-power processor systems and using adaptive scheduling to maintain energy efficiency across a number of processors. The non-academic partner (Leonardo) will be involved in defining the problem set and will help by supplying representative data for algorithm development and testing.

This project is part of the EPSRC Funded CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science.

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.

For informal enquires please contact Dr Angel Garcia-Fernandez or

To apply for this Studentship please submit an application for an Electrical Engineering PhD via our online platform ( 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



Dr Ángel García-Fernández - University of Liverpool

Ángel García-Fernández received the telecommunication engineering degree and the Ph.D. degree from Universidad Politécnica de Madrid, Madrid, Spain, in 2007 and 2011, respectively.

He is currently a Lecturer in the Department of Electrical Engineering and Electronics at the University of Liverpool, Liverpool, UK. He previously held postdoctoral positions at Universidad Politécnica de Madrid, Chalmers University of Technology, Gothenburg, Sweden, Curtin University, Perth, Australia, and  Aalto University, Espoo, Finland.

His main research activities and interests are in the area of statistical signal processing and data science. In particular, he has extensive experience in the development of multiple target tracking algorithms, non-linear Kalman filtering algorithms, and sequential Monte Carlo methods. He has worked in applications related to surveillance, advanced driver assistance systems, health technology, indoor localisation using smartphones, and machine learning classification.

To date, he has published 25 papers in top-ranking journals in these areas and he was recipient of the best paper award at the International Conference on Information Fusion in 2017.


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.