Research Associate / Postdoctoral position in machine learning and knowledge representation at the University of Edinburgh

A full-time post is available, fixed-term for one year

Data science provides many opportunities to improve private and public life, and it has enjoyed significant investment in the UK, EU and elsewhere. Discovering patterns and structures in large troves of data in an automated manner — that is, machine learning — is a core component of data science. It currently drives applications in computational biology, natural language processing and robotics.

However, such a highly positive impact is coupled to a significant challenge: how can we incorporate realistic constraints when training the model? How we can leverage the relational structure of the world?

Such questions are clearly vital for appreciating the benefits of AI. In the context of a recent Royal Society Fellowship on probabilistic relational models, we would like to advertise an opportunity to be a postdoc position on a research project, ideally starting April 1, 2020, full-time and spanning 12 months. In particular, the project is in the context of unifying logical methods (SAT, SMT, model counting) and probabilistic modelling and learning techniques, including deep learning. The outcome of this research would connect to topics such as explainable AI.

 Enquiries should be directed to: Vaishak Belle (

Information about the university and group can be found at and

Apply here

Closing date is 2 March 2020 at 5pm GMT.