Research Fellow in Ethical Machine Learning
School/department: School of Engineering and Informatics
Hours: Full time
Contract: Fixed term until 30th September 2018
Salary: Starting at £32,004 and rising to £38,183 per annum (starting salary will be set according to skills and abilities).
Placed on: 13 July 2017
Closing date: 10 September 2017. Applications must be received by midnight of the closing date.
Expected interview date: Mid September
Expected start date: 1 October 2017
A research position funded by the EPSRC is available in the group of Dr Novi Quadrianto on a project titled “EthicalML: Injecting Ethical and Legal Constraints into Machine Learning Models”. The long-term goal of the project is to develop a probabilistic machine learning framework with plug-and-play constraints that is able to handle fairness, transparency, and confidentiality constraints, their combinations, and also new constraints that might be stipulated in the future.
The successful candidate will participate in the research activities in the Predictive Analytics Lab (PAL) co-directed by Dr Quadrianto at Sussex. The successful candidate will engage in a number of exciting ongoing national and international research collaborations of PAL (e.g. with the Leverhulme Centre for the Future of Intelligence, universities in Cambridge, Sydney, Moscow, and Darmstadt) in the topics of privileged learning, Bayesian methods, and deep learning. In addition to undertaking high quality research and publishing in top machine learning conferences/journals including NIPS, ICML, JMLR, and TPAMI, the PAL group also creates significant impact by providing support, technology, and highly-trained specialists to a new generation of technology companies. It is expected that the successful candidate will take a leadership role in supporting the group activities including the supervision of Ph.D and MSc students.
The successful candidate will normally be of doctoral level or equivalent qualification. Research experience in machine learning is essential. Experience with Bayesian methods and non-convex optimization is a distinct advantage. Informal enquiries are welcome and can be made to Novi Quadrianto (N.Quadrianto@sussex.ac.uk).
How to apply
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