Research Fellow in Machine Learning Ref 9304

School/department: School of Engineering and Informatics – Department of Informatics
Hours: full time or part time hours considered up to a maximum of 1 FTE.
Requests for flexible working options will be considered (subject to business need).
Contract: fixed term until 31 October 2024
Reference: 9304
Salary: starting at £35,333 to £42,155 per annum, pro rata if part time
Placed on: 24 October 2022.
Closing date: 28 November 2022. Applications must be received by midnight of the closing date.
Expected Interview date: Second week of December
Expected start date: 1 January 2023


Job description

We are looking for a post-doctoral research fellow with a strong machine learning background to work with Profs Luc Berthouze and George Parisis on the recently EPSRC-funded project “Rethinking large-scale network management through the lens of neuroscience”. The project is one of 77 adventurous new projects funded by the EPSRC under the New Horizons initiative to explore high-risk speculative research ideas across Engineering and ICT.

Our project aims to transform the way ICT networks are being conceptualised for management, by developing a data-driven characterisation of emerging dependencies between ICT components inspired by recent neuroscientific paradigms used to study the brain and allowing to capture and act upon the functional impact of complex and changing interactions across layers and processes.

In the first phase of the project, the PDRA will develop and implement methods for inferring time-varying latent inter-dependencies based on events emitted, processed and stored in modern network and service deployments. Conceptual challenges to be met include the presence of multiple time scales as well as hierarchical organisation.

In the second phase of the project, the aim is to disambiguate hypothetical causal structures from the above statistical dependencies, with the view to provide interpretable and actionable insights. Use-case scenarios considered will be failure prediction and root-cause-analysis.

We are looking for a researcher with a proven record of developing and deploying machine learning / mathematical and statistical modelling in large interconnected systems (e.g., biological, social or technological networks). Strong technical skills are required. Befitting the interdisciplinary and high-impact nature of the project, the candidate should be willing to engage with both academic and industrial partners.

Please contact Prof Luc Berthouze, l.berthouze@sussex.ac.uk for informal enquiries.

The University is committed to equality and valuing diversity, and applications are particularly welcomed from women and black and minority ethnic candidates, who are under-represented in academic posts in Science, Technology, Engineering, Medicine and Mathematics (STEMM) at Sussex.

The University of Sussex values the diversity of its staff and students and we welcome applicants from all backgrounds.

You can find out more about our values and our EDI Strategy,  Inclusive Sussex, on our webpages.

Download job description and person specification Ref 9304 [PDF 221.29KB]

The University requires that work undertaken for the University is performed from the UK. 

Visa Sponsorship Queries:This role has been assigned an eligible SOC code and meets the salary requirements for Skilled Worker Sponsorship if full time. Please consult our Skilled Worker Visa information page  for further information about Visa Sponsorship.

Please note that this position may be subject to ATAS clearance if you require visa sponsorship. 


How to apply

Download our academic application form [DOC 199.50KB] and Personal Details and Equal Opportunities Form [DOC 119.50KB] and fill in all sections.

Email your completed application, and personal details and equal opportunities form, to enginfrecruitment@sussex.ac.uk  

You should attach your application form and all documents to the email in PDF format (we are unable to accept applications as google.docs or .pages) and use the format job reference number / job title / your name in the subject line.

You can also send your application by post to Human Resources Division, Sussex House, University of Sussex, Falmer, Brighton, BN1 9RH.


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