Visiting Assistant Professor Position, Statistics, REPOST

Location
Storrs, CT
Posted
Mar 21, 2017
Institution Type
Four-Year Institution
Job Title: Visiting Assistant Professor Position, Statistics, REPOST
Job ID: 2017300
Location: Storrs Campus
Full/Part Time:
Regular/Temporary: Temporary

Job Posting

The Department of Statistics at the University of Connecticut invites applications for a full-time Visiting Assistant Professor position to begin in August 2017.

The University of Connecticut (UConn) is entering a transformational period of growth supported by the $1.7B Next Generation Connecticut (http://nextgenct.uconn.edu/) and the $1B Bioscience Connecticut (http://biosciencect.uchc.edu/) investments and a bold new Academic Plan: Path to Excellence (http://issuu.com/uconnprovost/docs/academic-plan-single-hi-optimized_1). As part of these initiatives, UConn has hired more than 450 new faculty members at all ranks during the past three years. We are pleased to continue these investments by inviting applications for faculty positions in the Department of Statistics. For more information regarding the Department of Statistics, please visit the department website at www.stat.uconn.edu.

The successful candidate will be expected to share a deep commitment to effective instruction at the undergraduate and graduate levels and to the mentoring of students in their professional development. The successful candidate will be expected to broaden participation among members of under-represented groups; demonstrate through their activities the richness of diversity in the learning experience; integrate multicultural experiences into instructional methods and research tools; contribute to the development of pedagogical techniques designed to meet the needs of diverse learning styles and intellectual interests.

Minimum Qualifications

Ph.D. in statistics, biostatistics, or related field and experience in teaching as well as in research is required. The Department welcomes applicants from all areas of statistics.

Preferred Qualifications

Experience in applied statistics, applied probability, statistical computing, and closely related areas.

Appointment Terms

This is a full-time, 9-month, non-tenure track position with an anticipated start date of August 23, 2017. The appointment of the position is for two years with a possibility of extension. Responsibilities include teaching two undergraduate level courses per semester and conducting research. No administrative or committee service work is required. Salary will be commensurate with qualifications and interests.

To Apply

Select "Apply" and submit the following materials via Academic Jobs Online: a cover letter, curriculum vitae, teaching statement, and research statement. Additionally, please follow the instructions in Academic Jobs Online to direct three reference writers to submit letters of reference on your behalf. Employment of the successful candidate will be contingent upon the successful completion of a pre-employment criminal background check. (Search # 2017300)

The job posting is scheduled to be removed at 11:59 p.m. Eastern time on April 28, 2017.

All employees are subject to adherence to the State Code of Ethics which may be found at http://www.ct.gov/ethics/site/default.asp.

The University of Connecticut is committed to building and supporting a multicultural and diverse community of students, faculty and staff. The diversity of students, faculty and staff continues to increase, as does the number of honors students, valedictorians and salutatorians who consistently make UConn their top choice. More than 100 research centers and institutes serve the University's teaching, research, diversity, and outreach missions, leading to UConn's ranking as one of the nation's top research universities. UConn's faculty and staff are the critical link to fostering and expanding our vibrant, multicultural and diverse University community. As an Affirmative Action/Equal Employment Opportunity employer, UConn encourages applications from women, veterans, people with disabilities and members of traditionally underrepresented populations.

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