The Informatics Institute at the University of Amsterdam invites applications for a fully funded position for a PhD candidate in the area of causal modeling, reasoning and discovery, with a strong focus on applications in molecular biology.

The position is part of the VIDI project 'Causal Inference: Theory for Applications' funded by the Netherlands Organization for Scientific Research (NWO), and will be supervised by dr. Joris Mooij.

The successful candidate will be based in the Intelligent Autonomous Systems (IAS) group led by prof. dr. Max Welling, which is part of the Intelligent Systems Lab Amsterdam (ISLA) within the Informatics Institute (IvI) at the University of Amsterdam, the Netherlands. The institute was recently ranked among the top 50 computer science departments in the world by the 2011 QS World University IT Rankings.

ISLA consists of 20 members of faculty, 20 post-doctoral researchers, and more than 50 PhD candidates. Members of the lab are actively pursuing a variety of research initiatives, including machine learning, decision-theoretic planning and learning, causal discovery, multiagent systems, human-computer-interaction, natural language processing, information retrieval, and computer vision.

Project description
Many questions in science concern causal relationships. 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 focus on 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. This work will be done with a strong focus 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.