Research Associate

Charlottesville, VA
Jun 22, 2017
Institution Type
Four-Year Institution

Working Title: Research Associate

Position Type: Professional Research Staff

Department: Department of Systems and Information Engineering

Location: Charlottesville

Posting Date: 06-21-2017

Posting Summary:
The Data Science Institute (DSI) and the School of Engineering and Applied Science (SEAS) at the University of Virginia (UVA) are seeking a post-doctoral research associate to perform research on projects related to text analysis and to develop new methods and algorithms that use text from documents to predict behaviors by the individuals and groups that generated the documents.

Candidates must have a PhD or equivalent degree in Data Science, Systems Engineering, Computer Science or related disciplines.
Candidates must have expertise in supervised and unsupervised learning methods, preferably in text mining algorithms including topic analysis, word2vec, ensemble methods, and neural nets. Be proficient in programming languages such as python and R and their associated text mining libraries and packages and have experience in the application of machine learning techniques to unstructured data.

In addition, the selected candidate should have excellent oral and written communication skills to present at professional meetings and to supervise undergraduate and graduate students on multiple projects along with the PI and faculty members in the DSI. The position is for one year with the possibility of renewal, based on satisfactory performance.

To apply, visit and search on Posting Number 0621112. Complete a Candidate Profile online and attach a cover letter, CV and contact information for three references. Applicant review will begin on July 1, 2017 and the position will remain open until filled.

For additional information about the position, contact Donald E. Brown at

The University of Virginia is an equal opportunity/affirmative action employer committed to developing diversity in faculty and welcomes applications from women, minorities, veterans and persons with disabilities.

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