Job title - Research Associate in Reinforcement Learning Techniques
Job number - ACAD104192
Division/School - School of Computer Science, Electrical and Electronic Engineering and Engineering Maths
Contract type - Open Ended
Working pattern - Full time
Salary - £33,199 - £37,345
Closing date for applications - 28th October 2019
University of Bristol is seeking an exceptional candidate to take up a research position working on AI techniques for future digital infrastructure. The Next Generation Digital Converged Infrastructure (NG-DCI) project aims to lay the foundations for the next generation of internet infrastructure, creating an agile, resilient network capable of meeting future needs of our rapidly changing society.
Imminent developments such as Connected and Autonomous Vehicles (CAV), 5G and virtual reality will require a radical shift in the way our networks perform and how they are orchestrated and maintained.
Jointly funded by BT and the Engineering and Physical Sciences Research Council (EPSRC), the partnership brings together experts from business and academia, with specialist knowledge ranging from networking, communications, statistics and AI to industrial automation and organisational behaviour.
The project team includes partners from Universities of Bristol, Lancaster, Cambridge and Surrey, and aims to develop a future network that is “autonomic”, with the capability to react and even predict changes in networking demand, reconfiguring infrastructure accordingly with minimal human intervention.
This will lead to new services, improved customer experiences in terms of network reliability, and greater agility for businesses which need digital services that can adapt as they grow.
The partnership builds on long-term research collaborations between BT and the academic members.
Within this project the University of Bristol will lead an effort to co-create agile, resilient, self-organising networks meeting the future needs of CAVs, 5G and virtual reality.
The main research area for this post will be deep reinforcement learning techniques and development of communications networks simulation engines in the context of continuous control and self-organisation of networks.
It is anticipated that interviews will take place within 2 weeks of the closing date.
Informal enquiries can be made to: Professor Robert Piechocki.
We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and BAME communities, to join us.
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