Phd Studentship, Machine Learning Techniques in Optical Communication Networks
Engineering and Applied Science - Studentships
Location: Aston University Main Campus
Contract Type: Fixed Term (3.5 Years)
Basis: Full Time
Closing Date: 23.59 hours BST on Wednesday 01 May 2019
Interview Date: To be confirmed
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 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 in optical networks, including 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.
The position is available to start in 2019 (subject to negotiation) either July or October, or in 2020 in January or April (subject to negotiation).
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 but will need to find a way to cover the difference between the ‘Home/EU’ and the ‘Overseas’ tuition fees, currently this is £12,290 in 2018/19. As part of the application you will be required to confirm that you have applied for, or, secured this additional funding.
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.
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.
For formal enquiries about this project contact Sergei K. Turitsyn by email at email@example.com
Submitting an application
Details of how to submit your application, and the necessary supporting documents can be found here.
The application must be accompanied by a “research proposal” statement. An original proposal is not required as the initial scope of the project has been defined, candidates should take this opportunity to detail how their knowledge and experience will benefit the project and should also be accompanied by a brief review of relevant research literature.
If you require further information about the application process please contact the Postgraduate Admissions team at firstname.lastname@example.org
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