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Virginia Tech

Location: BlacksburgVA 24061 Document ID: AA505-0H4N Posted on: 2017-11-2011/20/2017 Job Type: Full-time

Job Schedule:Full-time
2017-12-20
 

Postdoctoral Associate in automated scientific discovery

Virginia Tech is a public land-grant university, committed to teaching and learning, research, and outreach to the Commonwealth of Virginia, the nation, and the world. Building on its motto of Ut Prosim (that I may serve), Virginia Tech is dedicated to InclusiveVT-serving in the spirit of community, diversity, and excellence. We seek candidates who adopt and practice the Principles of Community, which are fundamental to our on-going efforts to increase access and inclusion, and to create a community that nurtures learning and growth for all of its members. Virginia Tech actively seeks a broad spectrum of candidates to join our community in preparing leaders for the world.

Position Summary:
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 August 1, 2017.

Required Qualifications:
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.

Preferred Qualifications:
Programming proficiency (especially in Python) is desirable.



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