Research Associate - Engineering & Applied Science
Applications are invited for a full-time one year (with possibility of extension) Research Associate position, through EPSRC project grant EP/R035342/1 “Transforming networks - building an intelligent optical infrastructure” (TRANSNET), to be undertaken within the Aston Institute of Photonic Technologies (AIPT). The Aston Institute of Photonic Technologies pursues a diverse range of device-and-system-level topics at the leading edge of technology. The key research areas include high-speed optical transmission and processing, in fibre-based optical devices and components, nonlinear photonics, and in fibre optic sensors. It has recently successfully expanded its activities in a number of key areas including femtosecond pulsed laser techniques, medical sensing devices, and planar integrated optical circuits.
The successful applicant will join an established theoretical/experimental group working on the applications of the machine learning methods and, partially, nonlinear Fourier transform for signal modulation, transmission and processing, for the fibre nonlinearity mitigation and the development of the new generation of extra-high-capacity optical networks. The project implies the high-level mathematical expertise of the researcher and involves interaction with mathematicians, theoretical physicist, and optical engineers/industrial partners. You will have a PhD in a relevant subject.
Background of the Project
Optical networks underpin the global digital communications infrastructure, and their development has simultaneously stimulated the growth in demand for data, and responded to this demand by unlocking the capacity of fibre-optic channels. The next-generation digital infrastructure needs more than raw capacity - it requires channel and flexible resource and capacity provision in combination with low latency, simplified and modular network architectures with maximum data throughput, and network resilience combined with overall network security. How to build such an intelligent and flexible network is a major problem of global importance. The aim of TRANSNET is to address this challenge by creating an adaptive intelligent optical network that is able to dynamically provide capacity where and when it is needed - the backbone of the next-generation digital infrastructure. We propose to reduce the complexity of network design to allow self-learned network intelligence and adaptation through a combination of machine learning and probabilistic techniques. This will lead to the creation of computationally efficient approaches to deal with the complexity of the emerging nonlinear systems with memory and noise, for networks that operate dynamically on different time- and length-scales. This is a fundamentally new approach to optical network design and optimisation, requiring a cross-disciplinary approach to advance machine learning and heuristic algorithm design based on the understanding of nonlinear physics, signal processing and optical networking.