Postdoctoral Associate in automated scientific discovery
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|Working Title:||Postdoctoral Associate in automated scientific discovery|
|Faculty Rank:||Postdoctoral Associate|
|About Virginia Tech:||
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.
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.
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.
|Must have a conviction check:||Yes|
|How to Apply for this Job:||
Qualified applicants must electronically submit a letter of application, curriculum vitae, and at least two letters of recommendation to jobs.vt.edu. 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, email@example.com. Virginia Tech is committed to building a culturally diverse faculty and strongly encourages applications from women and minorities.
|Employee Category:||Research Faculty|
|If restricted or temporary, enter end date:||July 31, 2018|
|FLSA Status:||Exempt: Not eligible for overtime|
|Tenure Status:||Non-Tenure Track|
|Academic Year or Calendar Year:||Calendar Year|
|If part time, enter Percent Time:|
|Location, if not Blacksburg:|
|Location Zip Code:||24061|
|Job Posting Date:||12/15/2016|
|Review Begin Date:||03/15/2017|
|Job Close Date(if applicable):|
|Restricted to university employees only?|
|Equal Opportunity/Affirmative Action Statement:||
Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, gender, gender identity, gender expression, national origin, political affiliation, race, religion, sexual orientation, genetic information, or veteran status; or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees, or applicants; or any other basis protected by law.
For inquiries regarding non-discrimination policies, contact the executive director for Equity and Accessibility at 540-231-2010 or Virginia Tech, North End Center, Suite 2300 (0318), 300 Turner St. NW, Blacksburg, VA 24061.
|Reasonable Accommodation Statement:||
If you are an individual with a disability and desire accommodation please contact the hiring department.
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