Optical Theory Group

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 this position can be found here.

Please contact Matthew Foreman for further information.

The ability to detect single molecules and precisely monitor their behaviour is critical to understanding key processes in biology such as protein folding, DNA synthesis and membrane transport. Modern biosensing techniques often exploit the advantages offered by optical technologies, namely speed, flexibility and low cost. An important and powerful example is that of a whispering gallery mode (WGM) sensor in which light is confined in a dielectric microstructure and brought to repeatedly interfere with itself. As a result, only specific optical frequencies can be supported and reside within the cavity without suffering large losses. Sensitivity to single bioparticles has been demonstrated by monitoring perturbations to the spectral properties of optical WGMs and hence they represent an indispensable component of next generation high-performance optical biosensing.

In the quest to push the sensitivity limits to enable detection of smaller bioparticles, or facilitate earlier disease detection, we have recently considered advanced WGM sensing platforms, such as hybrid photonic-plasmonic systems and droplet resonators. In this PhD project a number of novel unconventional whispering gallery modes will be studied theoretically and their sensing characteristics established. Analytic techniques to enable quantification of bioparticles, including size, shape and enantiomer characterisation will be 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.

Development of quantitative techniques for measurement and monitoring of biological tissues is vital to improving healthcare and quality of life. Significant effort and resources have thus been invested to improve both the sensitivity and specificity of current bioimaging technology, with optical techniques at the fore. Predominantly, however, current methods are based on measurements of optical intensity or wavelength. Such measurements forego the additional information afforded by study of the degrees of freedom associated with the polarisation of light.

Polarization imaging modalities offer additional contrast mechanisms in biological imaging, such as quantification of collagen density through study of tissue birefringence or diattenuation. Furthermore polarisation measurements can reveal the micro-structure and composition of tissues, e.g. structural differences in elastin can result from burns, photodamage and/or the development of skin cancer. Although non-invasive in-vivo bioimaging methods are highly sought after, they can frequently be impeded by the need to image through relatively thick layers of highly scattering tissue, such as skin or breast tissue. Upon transmission of light through tissue the intensity and polarisation structure of the incident wave is modified, primarily due to scattering from e.g. cell nuclei or mitochondria, but also due to spatially varying birefringence and diattenuation from e.g. collagen networks. This PhD project will focus on theoretically establishing novel techniques for polarisation imaging through disordered media for example by exploiting polarisation correlations and higher order statistical properties, machine learning and informatics. The student will help build analytic models to describe evolution of polarised light in random media and analyse a number of key problems including control of local polarisation in deep tissue, localisation and orientational measurements of buried fluorescent molecules and determination of structural properties of scattering tissues. The ideal candidate has a keen enthusiasm for theoretical optics and an interest in development of new applied methodologies for bioimaging. 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.

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 Research Fellow 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.