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Machine Learning to Identify Unique Events in Sparse Hyperspectral Datasets (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 Sivananthan Laboratories, a small business specializing in state-of-the-art night vision, electron microscopy and hyperspectral imaging technologies.
Many characterisation methods used in materials science and structural biology involve data intensive acquisitions that can be multi-dimensional in nature (3 spatial dimensions, time and any number of spectral bands). Often the limitation in the precision of the data analysis is the damage to the sample being investigated — for example, a protein structure being imaged in an electron microscope is often destroyed before enough signal is generated to form a high resolution image. While each material being characterised optically, by X-rays, electrons, neutrons, or ions is in some way different from others, it must conform the well-established rules of chemistry — there are only a small number of possible atoms, crystal structure types and bonding mechanisms. By using existing structures that are more stable to the radiation being used to analyse them, we can provide a set of training data that could be used to interpret the much lower signals that are possible from the technologically more relevant, but thermodynamically less stable structures important for many current/future applications. The goal of this PhD project is to investigate machine learning approaches that may permit images from stable materials, obtained from a wide variety of methods, to be used to increase the ability of methods such as hyperspectral imaging in electron microscopy and X-ray systems to observe thermodynamically unstable materials and processes on the atomic scale. Such advances have the potential to significantly impact the search for new personalized medicines, the development of new advanced energy storage systems, and our ability to directly see chemistry important for catalysing environmentally friendly processes.
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.
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.

Funding Notes

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.

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