Research Fellow in Machine Learning Ref 5603 (2 posts available)

School/department:  School of Engineering and Informatics/Department of Informatics
Hours: Full time. Requests for flexible working working options will be considered (subject to business need)
Contract: The position is provided for one or two years. Extensions are possible.
Reference:  5603
Salary: Grade 7 starting at £33,797 to £40,322 per annum (pro rata, part-time)
             Grade 8 starting at £41,526 to £49,533 per annum (pro rata, part-time)
Placed on: 9 March 2021
Closing date: 23 March 2021. Applications must be received by midnight of the closing date.
Expected start date: ASAP


Job description

Applications are invited for a Research Fellow in Machine Learning (2 posts available) at the Predictive Analytics Lab (PAL) in the Department of Informatics at the University of Sussex. The duration of the position is originally for one or two years 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 Dr 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 in a field related to our research area such as approximate inference, reinforcement learning along with a good publication record in leading machine learning conferences (e.g. NeurIPS, ICML, ICLR). The successful applicant will have opportunities to visit our research lab BCAM Severo Ochoa Strategic Lab On Trustworthy Machine Learning in Bilbao, Spain. 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 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.

Download job description and person specification Ref 5603 [PDF 195.27KB]


How to apply

Download our academic application form [DOC 199.50KB] and Personal details and equal opportunities form [DOC 110.00KB] 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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