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PhD Studentship: Machine Learning Techniques in Optical Communication Networks

Aston University - School of Engineering and Applied Science

Qualification Type: PhD
Location: Birmingham
Funding for: UK Students, EU Students
Funding amount: £14,777 in 2018/19 (subject to eligibility)
Hours: Full Time
Placed On: 24th January 2019
Closes: 1st May 2019
Reference: R190029

PhD Studentship (3 years) for the EPSRC Programme grant TRANSNET

Closing Date: 01.05.2019

Supervisors: Sergei K. Turitsyn/David Saad

Applications are invited for a three year Postgraduate studentship, supported by the School of Engineering and Applied Science, to be undertaken within the Aston Institute of Photonic Technologies at Aston University. The studentship is offered in support of the EPSRC project grant EP/R035342/1 “Transforming networks - building an intelligent optical infrastructure” (TRANSNET).

The position is available to start in 2019 in either July or October, or in 2020 in January or April (subject to negotiation).

Financial Support

This studentship includes a fee bursary to cover the home/EU fees rate, plus a maintenance allowance of £14,777 in 2018/19 (subject to eligibility).  Applicants from outside the EU may apply for this studentship.

Background to 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.

Person Specification

The successful applicant should have a first class or upper second class honours degree or equivalent qualification in machine learning, advanced mathematics, physics, optical communications or relevant suitable degrees. Preferred skill requirements include:

  • Knowledge of the machine learning methods (supervised and unsupervised, deep neural networks, reservoir computing).
  • Experience in optical communications, digital signal processing, nonlinear signal processing, and nonlinearity mitigation methods.
  • Experience in statistical methods and information theory, in particular - in application to optical communications.

We would particularly like to encourage applications from women seeking to progress their academic careers. Aston University is committed to the principles of the Athena SWAN Charter, recognised recently by a prestigious Silver Award to EAS, and we pride ourselves on our vibrant, friendly and supportive working environment and family atmosphere.

Contact information

For formal enquiries about this project contact Sergei K. Turitsyn by email at

Submitting an application

Details of how to submit your application, and the necessary supporting documents can be found on the Aston University website here.

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