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 for 22 months
Reference: 9304
Salary: starting at £36,333 to £47,047 per annum, pro rata if part time
Placed on: 18 April 2023.
Closing date: 16 May 2023. Applications must be received by midnight of the closing date.
Expected Interview date: 30 May 2023 onwards
Expected start date: As soon as possible.
This advert was recently posted on 24 October 2022 – Previous Applicants need not apply.
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 one of 77 adventurous new projects recently 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 (e.g., temporal network based) characterisation of emerging dependencies between ICT components and allowing to characterise and act upon the functional impact of complex and changing interactions across layers and processes.
The primary aim will be to 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.
A secondary 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 jobapps@sussex.ac.uk
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You can also send your application by post to Human Resources Division, Sussex House, University of Sussex, Falmer, Brighton, BN1 9RH.