Applications are invited for a research studentship in the field of “Developing Solutions for Multimodal Heterogeneous Data Fusion”, leading to the award of a PhD degree. It is a collaborative effort (50:50) between the Diamond Synchrotron Light Source and Imperial College London. The work at Imperial is led by Prof. Daniele Dini in Mechanical Engineering and Dr Paul Quinn at Diamond as part of the broader project InFUSE (Interface with the future: underpinning science to support the energy transition), funded by the EPSRC and Shell. To be eligible for support, applicants must be “UK Residents” as defined by the relevant funding bodies. Please check your suitability at the following here.
Massive amount of data is being continuously generated at exceptional and increasing scales. This data has become an important and indispensable part of every economy, industry, organization, and individual. Data generation and analysis is obviously a key task for experiments performed at the Diamond Synchrotron Light Source; the handling of large datasets due to the heterogeneity in their formats is one of the major challenges faced by the scientific community in this area. There is a need for efficient data processing techniques to handle and analyse heterogeneous data, and also to meet the computational requirements to process this huge volume of data. Providing a new platform to perform such tasks is a priority for the success of new initiatives, such as the recently awarded Shell-Imperial-Diamond InFUSE EPSRC Prosperity Partnership programme, which targets the development of novel, sustainable solutions for energy production and consumption, aims to build on the availability of new generation of synchrotron sources with smaller X-ray beams, increased photon flux and better coherence to enable scientific discoveries through the development of beamline cells and protocols for concomitant integrated investigations.
The quest for new materials, solutions and devices to accelerate the energy transition depends on the development of new and efficient methods to blend multiple image modalities from multimodal/multiscale data sets. The objective of this PhD project is to develop an innovative framework for fusing heterogeneous data from multimodal experiments using advanced machine learning algorithms and tools. This fusion will not only allow parameter correlation and extrapolation but also lends to improving signal to noise and resolution based on the overall information content and will investigate solutions for working with limited dataset sizes which can occur with synchrotron measurements. The open-source code developed by the student will provide a unique workflow for dealing with heterogenous data sets for all synchrotron and lab users. The student will be supervised by Prof. Daniele Dini at Imperial and Dr Paul Quinn at Diamond as main supervisors. The supervisory team will be joined by experts in the handling of complex datasets and the development of supervised and unsupervised machine learning techniques for data analysis as part of InFUSE.
You will be an enthusiastic and self-motivated person who meets the academic requirements for enrolment for the PhD degree at Imperial College London. Applicants should hold or expect to obtain a First-Class Honours or a high 2:1 degree at Master’s level (or equivalent) in Mechanical Engineering, another branch of engineering, Materials, Physics, Chemistry or a related science. We expect you to have an enquiring and rigorous approach to research together with a strong intellect and disciplined work habits. An interest in molecular modelling and battery technology is essential. Good team-working, observational and communication skills are essential.
To find out more about research at Imperial College London in this area and the Diamond studentship scheme, go to:
InFUSE: https://www.imperial.ac.uk/shell-diamond-prosperity-partnership/
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/V038044/1
https://www.diamond.ac.uk/Careers/Students/Studentships.html
For information on how to apply, go to:
http://www.imperial.ac.uk/mechanical-engineering/study/phd/how-to-apply/
For further details of the post contact Prof. Daniele Dini [Email Address Removed] +44 (0)20 75947242. Interested applicants should send an up-to-date curriculum vitae to Prof. Dini. Suitable candidates will be required to complete an electronic application form at Imperial College London in order for their qualifications to be addressed by College Registry.
Closing date: until post filled
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