Two (2) PhD & One (1) Postdoctoral Positions in Causal Inference, University of Amsterdam, Netherlands (2015-2016)

Applications are invited for three fully funded positions (two PhD candidates and one postdoctoral researcher) to work with Dr. Joris Mooij on the development and validation of new methods for causal modelling, reasoning and discovery for systems involving feedback, with a strong focus on applications in molecular biology. The position is part of the ERC Starting Grant project 'CAFES: Causal Analysis of Feedback Systems', funded by the European Research Council. The successful candidates will be based in the Amsterdam Machine Learning Lab (AMLab) led by Prof. Dr. Max Welling within the Informatics Institute of the Faculty of Science of the University of Amsterdam, the Netherlands.

Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used to make predictions in situations where a system has been perturbed by an external intervention. The ability to reliably make such predictions is of great value for practical applications in a variety of disciplines. The research will consist of the development of new theory and efficient algorithms for robust discovery of causal relationships and estimation of causal effects from a combination of observational data, interventional data, and background knowledge. The focus will lie on the challenging but important class of systems that involve causal feedback. A strong emphasis also lies on applications in molecular biology, one of the most promising areas for automated causal discovery from data, enabling a thorough validation of causal prediction methods in practice. Successful applicants are expected to help develop this research line, to assist in teaching and in supervising Master’s students. In addition, the postdoctoral researcher is expected to assist in supervision of the PhD students.

Requirements

We are looking for highly motivated and creative individuals who enjoy working in a multidisciplinary research environment.

Applicants for the PhD candidate positions need:
- a Master's degree in computer science, artificial intelligence, mathematics, statistics, physics or closely related area;
- a solid understanding and working knowledge of modern probabilistic machine learning and statistics;
- hands-on experience with causal discovery methods or probabilistic graphical modelling will be a plus.

Applicants for the postdoctoral researcher position need:
- a PhD degree (or equivalent qualification) in computer science, statistics, applied mathematics, bioinformatics, physics, or a related field of study;
- an exceptional scientific track record, documented by publications at first-tier journals and conferences;
- a strong research background in causal inference, probabilistic machine learning or statistics.

In addition, all successful applicants should have:
- excellent mathematical skills (especially in probability theory and statistics, calculus, and linear algebra);
- excellent programming skills (preferably in at least one of the following languages: C++, Python, MatLab, R);
- strong communication, presentation and writing skills and excellent command of English;
- a strong interest in developing algorithms for applications on large-scale data analysis problems in biology;
- commitment and a cooperative attitude.

Further details:
http://www.academictransfer.com