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Assistant Professor in Theoretical Computer Science – Data Science

University of Massachusetts Amherst


Location:
Amherst, Massachusetts 01103
Date:
10/03/2017
2017-10-032017-11-02
Categories:
  • Education and Research
  • Programming and Applications
University of Massachusetts Amherst
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Job Details

Assistant Professor in Theoretical Computer Science – Data Science
College of Information and Computer Sciences
University of Massachusetts Amherst

The College of Information and Computer Sciences at the University of Massachusetts Amherst invites applications for a tenure-track faculty position in Computer Science for the 2018-2019 academic year. One opening is available for an Assistant or Associate level Professor in Theoretical Computer Science with a preference for candidates whose work is related to Data Science. 

Applicants must have a Ph.D. in Computer Science or a related area, and should show evidence of exceptional research promise. 

Review of applications will begin on December 15, 2017. We will continue to accept and review applications through the spring. Using the link below, all applicants should submit a cover letter, curriculum vitae, research statement, and statement of teaching interests, along with the names and contact information for references and links to papers that best represent their research/experience. See full ad for details on the number of references and papers required.

 http://umass.interviewexchange.com/jobofferdetails.jsp?JOBID=89975

 

The University is committed to active recruitment of a diverse faculty and student body. The University of Massachusetts Amherst is an Affirmative Action/Equal Opportunity Employer of women, minorities, protected veterans, and individuals with disabilities and encourages applications from these and other protected group members. Because broad diversity is essential to an inclusive climate and critical to the University's goals of achieving excellence in all areas, we will holistically assess the many qualifications of each applicant and favorably consider an individual's record working with students and colleagues with broadly diverse perspectives, experiences, and backgrounds in educational, research or other work activities. We will also favorably consider experience overcoming or helping others overcome barriers to an academic degree and career.

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