Research Assistant / Research Fellow / Senior Research Fellow in Intelligent, High-Capacity Optical Fibre Transmission Systems & Networks

As a part of research under the EPSRC funded projects EP/R035342/1 'Transforming networks - building an intelligent optical infrastructure (TRANSNET)' and EP/W015714/1 'Extremely Wideband Optical Communication Systems (EWOC)' being carried out by the Optical Networks Group, Department of Electronic and Electrical Engineering at UCL, we are looking to recruit three Research Fellows, at the levels of Research Assistant (Grade 6B), Research Fellow (Grade 7) or Senior Research Fellow (Grade 8), subject to experience. The TRANSNET research will focus on two aspects: (i) analysis & design of intelligent multiwavelength optical networks for a variety of transmission distances using machine learning/AI techniques and (ii) the application of machine learning (ML) techniques to develop a new family of adaptive optical transceiver technologies, tailored to the needs of a new generation of intelligent network architectures, capable of taking account of physical channel properties as well as and their experimental demonstration in different transmission scenarios. The EWOC research will focus on the design and testing of optical fibre transmission systems covering a 400 nm wavelength band (1270 - 1670 nm). The aims of the project are to investigate wideband, spectrally efficient data transmission, employing advanced signal formats and digital signal processing methods, with the aim of exploring the fundamental physical phenomena governing such systems and experimentally demonstrating world-record data throughputs for single-mode fibre systems. The posts will be co-funded by the Royal Society Professorship, 'Intelligent Optical Networks: Seeing through the Cloud'. About the role The work will include design of AI/ML algorithms, both network design and routing to maximise network throughput and the experimental implementation and testing of the systems. For the intelligent network post, the researcher will:

• Develop new AI/ML techniques for the analysis of core and data-centre network topologies
• Develop new AI/ML based routing algorithms to maximise network throughput and minimise delays
• Analyse the impact of network traffic statistics on network architectures and throughput
• Develop universal network tools covering core, metro and access architectures as well as data centres and within high-performance computer architectures
For the ML-based transceiver and transmission post, the researcher will participate in the design and development of intelligent, ML-based transceivers and extremely wideband optical fibre transmission, in which the researcher will:
• Develop and apply ML-based methods for optical signal generation and detection, equalisation and fibre nonlinearity mitigation (e.g., neural networks, learned digital back-propagation, deep-learned auto-encoders) and other digital signal processing techniques
• Design spectrally-efficient transmission systems exploiting the 1270nm - 1670nm wavelength band.
• Develop and use computer simulations of the systems, including the effects of fibre nonlinearity (e.g., SPM, XPM and stimulated Raman scattering).
• Carry out high-speed optical transmission experiments using straight line and recirculating loop testbeds in various transmission scenarios and explore the use of different sources, fibres, amplifiers, and nonlinear optical devices., Informal enquiries regarding this post can be addressed to Prof Polina Bayvel at
  
  Applicants should have a PhD (or about to submit) for Grade 7 & 8 and a good undergraduate degree in a relevant branch of science or engineering (Electronic Engineering, Information Engineering or Physics) for Grade 6B.
They should have strong knowledge of experimental research and hands on lab skills, including the use of advanced electronic and optical test instruments (e.g., spectrum analyser, oscilloscopes, arbitrary waveform generators), with experience and track record of hands-on experimental research in optical fibre transmission, including experience with DSP, advanced optical modulation formats and BER measurements. The successful candidate will have knowledge of noise, distortion/nonlinearity and crosstalk from electronic and optoelectronic devices, physical phenomena in optical fibre signal transmission (e.g., chromatic dispersion and nonlinearity). All applicants should possess strong programming skills in MATLAB and Python. Substantial experience of developing and using computer simulation tools. Experience developing programs implementing machine learning algorithms (e.g., use of TensorFlow, PyTorch, JAX) Those applying at Grade 8 should have significant post-doctoral experience on research (theoretical and experimental) associated with optical fibre communications.

2 of the posts are available from 1st January 2024 funded for 24 months in the first instance. One post is available for 36 months in the first instance. Further funding to support the posts may be available., As well as the exciting opportunities this role presents, we also offer some great benefits some of which are below:
• 41 Days holiday (27 days annual leave 8 bank holiday and 6 closure days)
• Additional 5 days' annual leave purchase scheme
• Defined benefit career average revalued earnings pension scheme (CARE)
• Cycle to work scheme and season ticket loan
• Immigration loan
• Relocation scheme for certain posts
• On-Site nursery
• On-site gym
• Enhanced maternity, paternity and adoption pay
• Employee assistance programme: Staff Support Service
• Discounted medical insurance
Visit to find out more. Our commitment to Equality, Diversity and Inclusion As London's Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world's talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong. We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL's workforce. These include people from Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women. You can read more about our commitment to Equality, Diversity and Inclusion here : https://www.ucl.ac.uk/equality-diversity-inclusion/ Available documents