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  Complexity-driven neuromorphic photonics in ultrafast nonlinear systems


   Department of Physics

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  Dr Juan Sebastian Totero-Gongora  No more applications being accepted  Self-Funded PhD Students Only

About the Project

Neuromorphic computing is a rapidly evolving research area aiming to overcome some of the standard limitations of current Machine Learning (ML) technologies. Today, ML is mostly implemented by programming networks of virtual neurons on standard computers. These systems can perform neural tasks through an iterative training process where the neuron-neuron connections are continuously adjusted until an optimised network configuration is reached. However, these solutions can be inadequate for real-time applications as they are affected by software latency, rapidly scaling power consumption and large supercomputer requirements.

Nonlinear photonics has recently emerged as a promising hardware platform for neuro-inspired computing [1]. In neuromorphic photonics, neurons and synapses are embodied by optical elements (e.g., miniaturised lasers and optical fibres) mimicking the response of a neural network [2]. When compared with electronics, neurophotonic devices have demonstrated a vast superiority regarding processing speed, data bandwidth and power consumption.

This PhD project involves the development of new types of neuromorphic photonic systems based on nonlinear wave interaction in one (or few) multimode optical elements, such as multimode optical fibers or integrated lasers. The objective is to devise strategies to feed data into, the optical system and leverage multi-mode interactions to emulate the response of an artificial neural network operating in the optical domain. The PhD will be ideally composed of theoretical/numerical and experimental research, but candidates interested in a theory-only PhD will be considered. We particularly welcome applications from under-represented groups, including, but not limited to BAME, disabled, neurodiverse, and female candidates.

The Research Group and environment

The PhD research project will be undertaken under the supervision of Dr Juan S. Totero Gongora, Senior Lecturer in Experimental Physics and EPSRC Quantum Technology Fellow. The research group is funded by a recently awarded £1M Quantum Technology Career Development Fellowship (https://www.ukri.org/news/securing-the-next-generation-of-quantum-technology-researchers/). 

The research group is part of the Emergent Photonics Research Centre (https://www.lboro.ac.uk/research/emergent-photonics) led by Prof. Marco Peccianti and Prof. Alessia Pasquazi. The Centre has been established in 2022 thanks to significant investment from Loughborough University in nonlinear photonics and quantum technologies. It comprises several research groups focused on micro-comb lasers (Prof. Pasquazi), terahertz imaging and communications (Prof. Peccianti), and machine learning control of high-power lasers and neuromorphic photonics (Dr. Totero Gongora). 

Supervisors

Dr Juan Sebastian Totero Gongora - [Email Address Removed]

Entry requirements for United Kingdom

Students should have, or expect to achieve, at least a 2:1 in Physics or Engineering. We will also consider atypical scientific/technological backgrounds.

English language requirements

Applicants must meet the minimum English language requirements. Further details are available on the International website.

Find out more about research degree funding

How to apply

All applications should be made online. Under programme name, select Department of Physics. Please quote the advertised reference number: PH/JTG-Un1/2023 in your application.

To avoid delays in processing your application, please ensure that you submit the minimum supporting documents.

Apply now


Computer Science (8) Engineering (12) Physics (29)

Funding Notes

UK fee
£4,596 full-time degree per annum
International fee
£25,100 full-time degree per annum
Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. University fees and charges can be paid in advance and there are several methods of payment, including online payments and payment by instalment. Fees are reviewed annually and are likely to increase to take into account inflationary pressures.

References

[1] G. Genty et al., ‘Machine learning and applications in ultrafast photonics’, Nature Photonics, pp. 1–11, Nov. 2020, doi: 10.1038/s41566-020-00716-4.
[2] M. A. Nahmias, B. J. Shastri, A. N. Tait, T. F. de Lima, and P. R. Prucnal, ‘Neuromorphic Photonics’, Optics & Photonics News, OPN, vol. 29, no. 1, pp. 34–41, Jan. 2018, doi: 10.1364/OPN.29.1.000034.

Where will I study?

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