The main objective of this research is combine spectroscopy with complementary computational modelling to yield unprecedented insight into structure-dynamics-function relationships in sunscreen filters. Gas-phase spectroscopy measurements will provide exquisite dynamical insight in the isolated environment; this information will then be used to guide the more complex solution-phase spectroscopy measurements that model more-closely the biological environment. Theory and computation will enrich the analysis and interpretation of the experimental data, while simultaneously guiding new experiments and suggesting unanticipated or unrecognised photoproducts. The DTP student will undertake the experiments on the plant sunscreen filter-based molecules (isopropyl sinapate, sinapoyl methyl lactate and sinapoyl malate) guided by Stavros (supervisor) with theory and computation being carried out with direction from Habershon (co-supervisor).

Warwick leads the Gravitational wave Optical Transient Observer (GOTO) project, which is the UK's premier wide-field sky-survey, aimed at discovering the electromagnetic counterparts to gravitational wave events. The project employs autonomous arrays of wide-field telescopes situated at two locations (La Palma, and Australia), that can patrol the sky regularly and respond to events of interest. While its main focus is to hunt for visible light signatures in response to gravitational wave detections, the projects offers a broad array of time-domain and transient science. Its design is particularly well suited to search for rare and rapidly evolving events. Objects of interest can then be fed to other telescopes for further characterisation and follow-up. The Phd research covers some instrumentation and control system work, high data-rate image reductions, sophisticated automated classification methods and science exploitation of a variety of sources such as neutron star mergers, supernovae, fast transients, counterparts to high-energy events etc. The work is within a larger team that includes other consortium members.

Weld repair of power generation steels remains a vital part of the life management process. The repaired components go on to experience service loading and therefore the creep behaviour of the weld repair regions needs to be understood. A key goal of this project is to link the observed damage in the heat affected zone of the post-creep test repair welds to the observed distribution of particles, grain size/features and other relevant pre-test microstructural observations in the repair welds. Low alloy and creep strength enhanced ferritic steels as well as dissimilar metal welds will be assessed.

Independent Component Analysis (ICA) is a signal processing technique that has gained in popularity over the years used as a means of extracting meaningful information from a number of biomedical signal measurements made across the body. In Neural Engineering in particular, ICA has provided a very useful means of extracting information about neural sources from recordings of the electroencephalogram and magnetoencephalogram - (EEG and MEG respectively). ICA is about the separation of statistically independent sources from a set of mixed measurements - for example in extracting eye-blinks from EEG, etc. The strong assumption of statistical independence of the underlying sources is usually well met in neural engineering cases. Standard methods of ICA usually work with multiple channels that extract a similar number of independent sources. In previous work it has been shown that 'single-channel' ICA is possible and is in itself a very powerful technique for the extraction of multiple sources underlying a single channel measurement. Whereas 'standard' ICA can be termed as 'spatial' ICA, single channel ICA can be termed as 'temporal' ICA - as there is no spatial information informing the ICA process due to the single channel arrangement. The logical progression for ICA is to perform spatio-temporal ICA, whereby the ICA process is informed by means of both spatial and temporal/spectral information derived from a set of neural signal recordings. It can be shown that space-time ICA results in a powerful algorithm that can extract meaningful information in brain signal recordings across a number of conditions. The technique is not without its issues, suffering from the same problems standard ICA suffers from; including issues around the assumptions of linear, noiseless, statistically independent mixing of sources as well as the dilemma of choosing relevant sources after ICA is complete. With space-time ICA the problem is compounded due to the curse of dimensionality. This project will build on previous work, enhancing the ICA process and applying the techniques to various EEG signal databases. A further part of the project will involve the setting up and running of various EEG data gathering exercises applying the ICA techniques to brain-computer interfacing paradigms.