This PhD project is part of the CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science.
The University of Liverpool’s Centre for Doctoral Training in Distributed Algorithms (CDT) 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.
This is a funded PhD position in biomedical imaging and deep learning, suited to candidates with an applied mathematics, computer science, electrical engineering, medical imaging, biomedical engineering, physics or equivalent MSc/BSc degree. The successful PhD student will be co-supervised and work alongside our external partner Sivananthan Laboratories, a small business specializing in state-of-the-art night vision, electron microscopy and hyperspectral imaging technologies.
The ability to acquire increasingly higher speed and higher spatial resolution images is the dominating factor driving the design and implementation of new and improved detectors for a wide variety of applications – everything from tracking livestock on farms, to satellites, to the most advanced scientific experiments. While the ability to “see” the raw images that have been acquired by a detector is something that every human likes to be able to do, in the vast majority of cases the images are subsequently analysed to extract quantitative information on what the image is or what is happening during the image acquisition. For example, in a transmission electron microscope (TEM), a series of images is usually analysed to extract the type, number and rate of change of atomic/molecular interactions taking place during a particular chemical reaction and once you have that data, you don’t need the images anymore. If it were possible to remove the imaging step in this process, and move straight to the analytics, the process of tracking dynamic events in the microscope (or in any imaging system) could be rapidly accelerated.
The goal of this PhD project is to develop the artificial intelligence (AI) methodology to extract quantitative analytics from images that contain the minimum amount of information. This will be accomplished by acquiring images using compressive sensing (CS), by which a small subset of random pixels in the image is acquired. Rather than reconstruct the full image, as is typical of CS, the goal will be to use the sub-sampled dataset to extract quantitative information on the entire dataset.
It is anticipated that the interpretation of the dataset will incorporate aspects of training data and machine learning, with the final output being the development of situation (technique) dependent imaging tools. This project will work in collaboration with other imaging projects in the CDT that are aimed at improving the ability to perform advanced TEM experiments.
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.
Students are based at the University of Liverpool and part of the CDT and Signal Processing research community. Every PhD is part of a larger research group which is an incredibly social and creative group working together solving tough research problems. Students have 2 academic supervisors and an industrial partner who provides co-supervision, placements and the opportunity to work on real world challenges. In addition, students attend technical and professional training to gain unparalleled expertise to make a difference now and in the future.
This studentship is due to commence 1 October 2021 (Covid-19 Working Practices available).
For any enquiries please contact the academic supervisors: Dr Yalin Zheng: [Email Address Removed], Professor Ke Chen: [Email Address Removed] and Professor Nigel Browning: [Email Address Removed] in the first instance or visit the CDT website for Director, Student Ambassador and Centre Manager details.
To apply please visit the CDT website for application instructions, FAQs, interview timelines and tips.
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