Optical Theory Group

Polarisation variations in a focused beam are not restricted by the traditional diffraction limit and superoscillatory, i.e. very high spatial frequency, variations are possible. Engineering of focused polarisation structures can be achieved, for example, through wavefront shaping and polarisation control of incident beams. Illumination of proteins or other small bioparticles with such superoscillatory polarisation structured light offers opportunities in detection of conformational changes or nano-displacements which can observed with high sensitivity in polarisation space, even though there is no observable variation in the intensity domain. In this project we seek to determine optimal polarisation structures and determine expected sensitivity through analytic and computational means. Viability of experimental detection will be assessed and potentially pursued. More details and the application link for this project can be found here.

Please contact Matthew Foreman for further information.

Digital molecular assays aim to detect, identify, and quantify different molecular species in a massively parallel manner. They therefore rely on the ability to execute potentially millions of single molecule assays, each of which can be read out individually. Optical imaging offers a natural, fast and inherently digitised readout whereby ultimately each individual pixel could correspond to readout of different molecular assays. Within the field of imaging, great efforts are often spent on obtaining high quality images possessing a large fidelity with the original scene, such that design criteria are based on improving aberration tolerances or resolution. However, image fidelity is often of secondary importance in digital molecular analytics, where the task is binary detection or identification of individual molecular species. In such scenarios an informatic approach, whereby an imaging system is considered as a channel through which information is transmitted, is more appropriate. The recorded image is then treated as a message from which we determine the presence or absence of target analyte molecules via subsequent processing. In this project, the principles of information theory, statistical signal estimation and noise modelling will be applied to design and optimisation of hybrid and multimodal imaging systems with a view to performing robust and quantitative digital molecular analytics. Performance of diagnostic systems will be assessed in terms of entropy, Fisher information, and other related informatic metrics. Estimation, signal processing and machine learning algorithms will be developed for optimal data fusion in multi-modal imaging and sensing systems. The ideal candidate has an enthusiasm for informatics, statistical analysis and algorithm development. They would have a first degree in physics, engineering, or mathematics with strong analytical, mathematical and programming skills. More details and the application link for a Research Fellow position for this project can be found here. PhD applications please get in touch via email sending a copy of your CV and degree transcript.

Please contact Matthew Foreman for further information.

Complex and random nanophotonic networks, formed for example by a web of interconnected optical waveguides, are an emerging optical technology offering a unique and novel approach to light transport, lasing, data processing and optical control. Intrinsically, light propagating in an optical network, or graph, undergoes multiple scattering, which can lead to a diverse range of phenomena such as Anderson localisation, loop resonances and long range correlations. Moreover, optical modes formed by recurrent scattering and interference, are highly sensitive to network topology, connectivity, scattering node properties and the balance of optical losses and gain. In our group we have recently shown that multiple scattering in a quasi-two dimensional optical systems, can be leveraged to significantly enhance the sensitivity of optical single molecule biosensors when these cooperative and localisation effects are appropriately engineered. Complex photonic networks therefore represent a promising, yet unexplored, platform for sensing which will be explored in this project.

During the course of this project, the candidate will develop simulation models for light propagation in random and complex photonic networks and study the statistical properties of different network ensembles. They will also formulate analytic theory accounting for bulk and localised perturbations to network geometry, material properties and scattering characteristics and generate supporting numerical data to quantify and enable optimised performance. Enhancements of light-matter interactions will be theoretically investigated and the use of spectral transmission properties for network fingerprinting established. The ideal candidate has a keen enthusiasm for theoretical optics and an interest in development of new applied methodologies for optical sensing. They would have a first degree in physics, engineering, or mathematics with strong analytical, mathematical and programming skills.

Please contact Matthew Foreman for further information.