This studentship has been developed by the University of Liverpool and STFC’s Hartree Centre in partnership with Sivananthan Laboratories, 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 datastream. 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 level of sub-sampling that be used 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 goal is to develop a coherent framework that can be used in the design of optimized imaging hardware with embedded algorithms.
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.
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-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.
These studentships are open to UK & EU Students and are fully funded for four years and will provide tuition fees and maintenance at the UKRI Doctoral Stipend Rate (£15,009 per year for 2019/20 rate).
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.
Successful candidates will also have experience of research within Maths, Engineering or Computer Science areas as well as good communication and writing skills.