Research Fellow in Machine Learning Ref 20680
School/department: School of Engineering and Informatics/Department of Informatics
Hours: Full-time or part-time hours considered up to a maximum of 1.0 FTE. Requests for flexible working options will be considered (subject to business need).
Location: Brighton Uk
Contract: Fixed Term. The position is provided for one year. Extensions are possible.
Salary: starting at £44,414 to £52,841 per annum pro rata if part-time.
Placed on: 13 June 2023
Closing date: 31 July 2023. Applications must be received by midnight of the closing date.
Expected Interview date: To be confirmed.
Expected start date: To be confirmed.
Applications are invited for a Research Fellow in Machine Learning at the Predictive Analytics Lab (https://wearepal.ai/) in the Department of Informatics at the University of Sussex. The duration of the position is originally for one year with the possibility of an extension. The position is part of the ERC funded project “Bayesian Models and Algorithms for Fairness and Transparency” (BayesianGDPR) – led by Professor Novi Quadrianto.
It involves the development of novel inference and computational methods towards the realisation of fair and transparent machine learning systems in static and dynamic settings. In particular, the project will focus on Bayesian methods and their “deep” extension. The successful candidate will develop and apply a range of techniques in variational inference, generative models, and reinforcement learning.
The successful applicant should have a PhD in machine learning along with a good publication record in leading machine learning conferences (e.g. NeurIPS, ICML, ICLR). The salary offered will be appropriate to the qualifications, standing and experience of the successful candidate.
Informal enquiries are welcome and can be made to Professor Novi Quadrianto (N.Quadrianto@sussex.ac.uk).
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.
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 [DOCX 65.11KB] and fill in all sections.
Email your completed application, and personal details and equal opportunities form, to firstname.lastname@example.org
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.