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.
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.
We are looking for a highly motivated and creative individual who enjoys working in a multidisciplinary research environment. Applicants must have:
In addition, a successful candidate should have:
Informal inquiries can be made to:
The appointment will be on a temporary basis for a period of 4 years (initial appointment will be for a period of 18 months and after satisfactory evaluation it can be extended for a total duration of 4 years) and should lead to a dissertation (PhD thesis). An educational plan will be drafted that includes attendance of courses and (international) meetings. The PhD student is also expected to assist in teaching of undergraduates.
Based on a full-time appointment (38 hours per week) the gross monthly salary will range from €2,083 in the first year to €2,664 in the last year. The Collective Employment Agreement (CAO) of the Dutch Universities is applicable.
Preferred starting date: 1 November 2014 (later starting date is possible).
Some of the things we have to offer:
English is the working language within the Informatics Institute. Since Amsterdam is a very international city where almost everybody speaks and understands English, candidates need not be afraid of the language barrier.
Applications should be submitted to application-science@uva.nl. To process your application immediately, please quote vacancy number 15-003 and the position you are applying for in the subject-line.
Applications must include a cover letter, curriculum vitae, a copy of your masters thesis, transcripts of university grades, and the names and contact information of two academic references (please do not include any recommendation letters). All these should be grouped in a single PDF attachment.
Please make sure to include one or two samples of independent work of yours, which demonstrate your best efforts and abilities in line with the requirements of this call. This could be published papers, term papers for projects, MS or BS thesis, samples of machine learning related code, or links to a website where such material can be downloaded.
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