CfA: Postdoctoral Research Associate in causal learning and automated scientific discovery (Virginia Tech)

Submitted by Benjamin Jantzen (Virginia Tech).

Postdoctoral Research Associate in causal learning and automated scientific discovery

A 1-year postdoc position (with the possibility of renewal for an additional year) is available in the area of causal learning and automated scientific discovery. The successful applicant will explore the formal connections between causal learning in the graphical causal modeling tradition and the algorithmic determination of natural kinds (classes of causal structures that support law-like generalizations useful for prediction and control). This work is part of a larger project to develop methods for learning natural kinds and for discovering novel features (or variables) of scientific relevance. This project offers the opportunity to participate in active collaborations with ecologists, climate scientists, bioengineers, and cognitive scientists. The mentor for this position is Dr. Benjamin Jantzen, Assistant Professor of Philosophy and Assistant Professor of Computer Science (by courtesy) at Virginia Tech in Blacksburg, VA. The position start date is June 1, 2017.

Candidates must have a Ph.D. in computer science, formal philosophy, applied math, statistics, or other related field at the time of appointment and a strong background in machine learning or graphical causal modeling. Candidates must have a rank-appropriate record of scholarship and collaboration in research on computational approaches to empirical learning, broadly construed. Programming proficiency (especially in Python) is desirable.

Qualified applicants must electronically submit a letter of application, curriculum vitae, and at least two letters of recommendation to Apply to posting #SR0160187. Applicant screening will begin on March 15, 2017 and continue until the position is filled. Inquiries should be directed to Dr. Benjamin Jantzen, Search Committee Chair, Virginia Tech is committed to building a culturally diverse faculty and strongly encourages applications from women and minorities.

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