Start date and duration October 2020 for 3.5 years
Overview The emergence of X-ray free electron lasers (X-FELs) has made it possible to follow chemical dynamics with structural resolution. However, this extension of experimental capabilities also calls for a simultaneous development in theory to help interpret the underlying structure and dynamics encoded within the experimentally obtained spectra. In this project, we will focus upon developing the predictive power and speed of a newly developed artificial neural network (ANN) to deliver a novel machine learning software to predict X-ray spectra which will allow the rapid analysis of experimental data. It will be used to analyse existing experimental data and predict potential new experiments.
Sponsor Science and Technology Facilities Council and Newcastle University
Eligibility Criteria The award is available to UK/EU applicants only.
You must have, or expect to achieve, at least a 2:1 honours degree or international equivalent, in Chemistry, Physics or Computing Science and an interest in coding.
How to apply You must apply through the University’s online postgraduate application system (https://bit.ly/39VKyhn).
You will need to:
Insert the programme code 8100F in the programme of study section Select ‘PhD Chemistry – Chemistry’ as the programme of study Insert the studentship code NES030 in the studentship/partnership reference field attach a covering letter and CV. The covering letter must state the title of the studentship, quote reference code NES030 and state how your interests and experience relate to the project Attach degree transcripts and certificates and, if English is not your first language, a copy of your English language qualifications You should also send your covering letter and CV to Dr Tom Penfold.