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Senior Research Associate (Future Media Experience Infrastructure) x 4 posts

School of Computing and Communications
Salary:   £34,804 to £40,322
Closing Date:   Sunday 02 May 2021
Interview Date:   To be confirmed
Reference:  A3334

We are seeking up to four post-doctoral researchers to work on an exciting new Prosperity Partnership in collaboration with the BBC and University of Surrey. 


The way in which media experiences are produced and delivered, from television and films to video games, is rapidly changing. In television and broadcast media, the prevalence of Internet-based delivery supports the independent transport of different parts of a stream – including audio, video, and additional media experience components – to be composed together at the point of playback. This paradigm is enabling new forms of hyper-personalised and immersive story-telling, and represents both opportunities and new challenges in the network delivery of these experiences.  

The Prosperity Partnership 

This exciting new Prosperity Partnership will address the key challenges for personalised content creation and delivery at scale using AI and Object-Based Media (OBM). The ambition is to enable media experiences which adapt to individual preferences, accessibility requirements, devices and location.  

The partnership builds on the BBC’s pioneering work in OBM and its ability to run large-scale trials with its audience and programme content. University of Surrey’s expertise in audio-visual AI for machine understanding of captured content will allow efficient creation of personalised OBM experiences. Lancaster University’s expertise in software-defined networking will develop adaptive systems for delivery of personalised experiences to millions of people whilst maintaining cost and energy efficiency. 

The Research at Lancaster 

At Lancaster we are studying how the world’s first hyper-adaptive end-to-end delivery infrastructure can be built to deliver personalised object-based media on an unprecedented scale. By combining novel systems-building technology with advanced real-time machine learning, this infrastructure will continually monitor popular content at a regional and national level, along with end-user personalisation preferences, to constantly drive the delivery system towards a more optimal form.  

Key Research Directions 

We have up to four post-doctoral research positions available to explore one or more of the following research themes: 

1. Novel Self-Adaptive Distributed Systems Paradigms: The vision of this project requires new approaches to building distributed systems, in which self-adaptive behaviours are at the core of the system’s capability. Individual pieces of code will be highly mobile, able to move from the cloud to the edge and back, or even be resident on programmable SDN network devices where needed, depending on which permutation brings the highest benefit at any given time. This will demand new approaches to system construction, state management, and overall system management – all of which must provide guarantees of soundness for production-class systems. 

2. Distributed Reinforcement Learning: Our overall platform will represent a very large distributed search space for real-time reinforcement learning, in which one node must work with all other nodes to take local actions that contribute to a global reward. This represents a point in the real-time learning field that has received little research for real-world systems. This track of research will investigate the state of the art in distributed reinforcement learning and seek to develop new techniques that scale well for state-of-the-art real-world stream processing systems. 

3. Orchestration Methods, Models, and Languages: A highly-adaptive distributed system covering thousands or tens of thousands of devices requires novel methods of orchestration so that system administrators can set policies and guide autonomous deployment decisions in desirable directions from a business perspective. This research track will therefore examine how orchestration models or languages that can be understood by administrators or even business executes, and which can be used with a highly adaptive deployment infrastructure to provide policies, guidelines, and constraints while still supporting high degrees of autonomous freedom. 

4. Nation-Scale Analytics and Monitoring: All of the above tracks are based on the ability to analyse and monitor the current state of a very large scale system, and to feed observations into orchestration and learning. This track will develop novel methods around how the network and distributed system is analysed on a continuous basis, at very large scale, and in a way that presents the information that is really needed by learning and orchestration. This includes adaptive data aggregation techniques to filter unneeded data, adaptive classifiers which can extract the right information to inform learning decisions, and human presentation techniques through which human users can explore the data and potentially suggest new classification techniques or trend analyses. 

Besides these directions, we also welcome your input and creativity in complementary research that fits within the project – we actively encourage self-led research in conjunction with project deliverables. 

Experience Needed 

The ideal candidate will have knowledge of one or more of the above topics – from distributed systems to reinforcement learning algorithms, orchestration techniques, and systems analysis. While existing knowledge in these areas is desirable, we are also able to offer guidance and support on each of these areas of research and welcome applications from general computer science researchers. The candidate will be expected to implement and test systems and algorithms on a daily basis throughout the project and so good programming skills are highly desirable. 


You will join a team led by three academic staff members at Lancaster, working closely with the BBC and University of Surrey. At Lancaster, you will be part of an established group of PhD students and post-docs performing research in networked and distributed systems, self-adaptive software, and operating systems. 

You will join us on an indefinite contract however, the role remains contingent on external funding which, at this time, is for a period of 36 months. 

If you have informal enquiries, please contact Professor Nicholas Race (  

We welcome applications from people in all diversity groups.


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Further details:

The University provides an environment that strongly supports the individual needs of each employee, whilst promoting the importance of wellbeing for all our colleagues. We have a range of support networks available for our employees, and more information on these can be found on the ‘Working at Lancaster’ information page.


We are committed to family-friendly and flexible working policies on an individual basis. The University recognises and celebrates good employment practice undertaken to address all inequality in higher education.


We warmly welcome applicants from all sections of the community regardless of their age, religion, gender identity or expression, race, disability or sexual orientation, and are committed to promoting diversity, and equality of opportunity.



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