Job openings: 2 Post-doc and 6 PhD students in Machine Learning and Computer Vision at the Predictive Analytics Lab, Department of Informatics, University of Sussex, UK

Project: BayesianGDPR ("Bayesian Models and Algorithms for Fairness and Transparency"), ERC Starting Grant (2020-2025)

Application deadline: There is no fixed deadline, applications will be considered until all positions are filled.

Job description:

There are several open positions for postdoctoral researchers and PhD students in the scope of the ERC project BayesianGDPR ("Bayesian Models and Algorithms for Fairness and Transparency"). It involves the development of novel inference and computational methods towards the realisation of fair and transparent machine learning and computer vision systems in static and dynamic settings. In particular, the project will focus on Bayesian methods and their "deep" extension. Three research directions are:

  1. Fairness under uncertainty in a static setting. Developing a machine learning framework for addressing fairness in classification problems and beyond, and under uncertainty about data, models, and predictions about future data.
  2. Fairness under uncertainty in a dynamic setting. Extending the framework to a setting where data points arrive over time, and models have to be dynamically updated when taking general feedback.
  3. Transparency in fairness. Ensuring a human could understand how non-discrimination is defined and achieved by using, among others, uncertainty estimates for building interpretable models and/or explicitly explaining about changes being made to the models to enforce non-discriminatory principles.

The specific roles assigned to the project members will take into account their research interests and their background.

The postdoc is originally for one or two years with the possibility of an extension. The salary range is £41,526 - £49,553 (Grade 8), depending on the candidate's experience. The successful postdoc 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.

The PhD studentship is a 4-year position, open for EU/UK and international students. Students interested in this topic will be supervised by Novi Quadrianto. Potential co-supervisors include Viktoriia Sharmanska , Ivor Simpson, and Jeremy Reffin. You are welcome to indicate your preferred choice of co-supervisor.

If interested in any of these positions, please send an email with your CV to n.quadrianto AT Feel free to contact me for any further information.

Working environment: The University of Sussex is a leading research- intensive university established in 1961. The Department of Informatics at Sussex is highly rated for its teaching and research. Its researchers work in an environment that was deemed to be wholly 4*/3* (world-leading/ internationally excellent) in the REF 2014. The department is currently hosting 4 ERC grants.

The PAL laboratory at the Department of Informatics was co-founded by Novi Quadrianto and Jeremy Reffin in 2017. Members of the laboratory undertake high quality research and publish in top machine learning, computer vision, and artificial intelligence conferences/journals including NeurIPS, ICML, CVPR, ICCV/ECCV, AAAI, JMLR, and TPAMI. The laboratory also creates significant impact by providing support, technology, and highly-trained specialists to a new generation of technology companies. The PAL group is growing in size and currently consists of 12 team members, consisting of faculty, researchers and research students.

The city of Brighton & Hove has everything - sun, sea, brilliant clubs, Premier League football club, great places to eat, fabulous shops, a truly cosmopolitan vibe and is located only 50min from central London. Located on the beach (only 30min by cycle from the University), Brighton boasts beautiful seaside views and beaches, boating, sports and beach activities. The South Downs provide breathtaking views, tranquil walks and plenty of opportunities for mountain biking, hiking or picnics.