EPSRC Funded Project: Optimized Sampling Approaches for Compressive Sensing in Multi-Dimensional Datastreams (EPSRC CDT in Distributed Algorithms)


The EPSRC Centre for Doctoral Training in Distributed Algorithms (DACDT) is pleased to be able to advertise this exciting studentship in conjunction with the University of Liverpool and in partnership with Sivananthan Laboratories in the US, a small business specializing in state-of-the-art night vision, electron microscopy and hyperspectral imaging technologies.

The current state-of-the-art in imaging hardware involves the very precise synthesis and fabrication of semiconducting materials into extended cameras that can now contain up to 64M pixels with a cost that can exceed £1M per device. In most cases, these high sensitivity cameras are implemented to detect signals that are very close to the noise level and as an added complexity are typically looking to characterise dynamic events (i.e. they need to be able to quantify the motion of fast-moving objects). The data per image frame in these systems can easily exceed 1TB, meaning that cameras currently have to operate in short bursts, have delayed responses due to the extended transfer of the data, and it can take days/months/years for image analytics to operate and identify key elements in the data stream. Obviously as the global economy pushes towards more automation and the use of remote sensing devices, these limitations have to be overcome.

One approach that can alleviate a large number of the problems associated with speed and precision in state-of-the-art imaging systems, is the use of Compressive Sensing (CS) methods. In the CS approach, a small subset of random pixels in the image in acquired and used to reconstruct the full dataset. This immediately reduces the amount of data and increases the imaging speed by the amount of sub-sampling that is used.

The goal of this PhD project is to determine the minimal level of sub-sampling that will be sufficient to reconstruct images from such diverse sources as satellites, night vision goggles and scanning transmission electron microscopes. By developing and implementing new algorithms for the specifics of the image contrast mechanism and its resolution limits, the ultimate goal is to develop a coherent framework that can be used in the design of optimized imaging hardware with embedded algorithms.

The project is closely related to the currently popular method of super-resolution using a single image (i.e. no massive training data required) and in the context of deep learning of artificial intelligence. The new mathematical tools of nonlinear and non-convex optimization techniques will be explored and developed. 

This project presents numerous opportunities to travel and interact with small and large businesses working on imaging technologies in the UK and around the world.

Applicants should have, or be expected to obtain, a minimum of a 2.1 undergraduate degree, Masters qualification or hold exceptional work experience in a related field such as mathematics, physics, engineering and/or computer science.

Successful candidates will also have some experience of research / projects in mathematics, computer science or engineering field. Importantly they must have a good background knowledge either in Mathematics (optimization, calculus of variations and iterative methods for nonlinear systems) or in computer science or data sciences (Python or C++ programming, machine learning and deep learning algorithms) as well as good communication and writing skills.

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 20+ external 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 distributed algorithms; the technical and professional networks needed to launch a career in next generation data science and future computing; and the confidence to make a positive difference in society, the economy and beyond.

Students are based at the University of Liverpool and part of the DA CDT and Signal Processing research community. Every PhD has 2 academic supervisors and an industrial partner who provides co-supervision and placements and also offers DA CDT students the opportunity to work on real world challenges. In addition, students attend cohort and individual technical training to gain unparalleled expertise, work across academic disciplines in highly sought-after topic areas and have the opportunity to attend UK and international conferences. A large number of events and training sessions are offered allowing DA CDT students to build personal and professional skills that will complement the technical training to give you the confidence and tools to make a difference now and in the future.

For informal technical enquires please contact Prof Ke Chen  or Dr Yalin Zheng   

For general application process queries please contact Kelli Cassidy 

To apply for this Studentship please follow the DA CDT Application Instructions. Submit an application for an Electrical Engineering PhD via the University of Liverpool’s online PhD application 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/distributed-algorithms-cdt/apply/


Open to EU/UK applicants

Funding information

Funded studentship

This project is a funded PhD Studentship for 4 years in total and will provide UK tuition fees and maintenance at the UKRI Doctoral Stipend rate (£15,285 per annum, 2020/21 rate).