Computer Science & Engineering, University of Connecticut

Position ID:UConn-CSE-2019099 [#12030]
Position Title: Assistant Professor
Position Type:Tenured/Tenure-track faculty
Position Location:Storrs, Connecticut 06269, United States [map]
Subject Area: Computer Engineering / Systems
Appl Deadline: (posted 2018/09/19, listed until 2019/03/19)
Position Description:    

The Computer Science and Engineering (CSE) Department at the University of Connecticut invites applications for a tenure-track faculty position at the assistant professor level. The position is expected to start on August 23, 2019. The department is seeking a Computer Scientist specializing in Machine Learning, Data Mining or Systems. 

The University of Connecticut (UConn) is entering a transformational period of growth supported by the $1.7B Next Generation Connecticut (http://nextgenct.uconn.edu/), 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 five years.  We are pleased to continue these investments by inviting applications for this new position. The Department of Computer Science & Engineering harbors a rich environment of instruction and research, offering three rigorous degrees (B.S., M.S., and Ph.D.) in the computing sciences and a world-class research enterprise. Additional information about the department can be found at http://www.cse.uconn.edu/.

Successful candidates will be expected to develop and sustain an internationally-recognized and externally-funded research program in Computer Science with specialization in the fields of Machine Learning, Data Mining or Systems.  Successful candidates must share a deep commitment to effective instruction in Computer Science at the undergraduate and graduate levels as well as development of innovative courses and mentoring of students in research, outreach and professional development.  Successful candidates are also expected to broaden participation among members of under-represented groups; demonstrate through their teaching, research and/or public engagement the richness of diversity in the learning experience; and provide leadership in developing pedagogical techniques designed to meet the needs of diverse learning styles and intellectual interests.

MINIMUM QUALIFICATIONS

Candidates must have an earned Ph.D. in Computer Science or a related field by the time of appointment; an established record of research in computing sciences with a specialty in Machine Learning, Data Mining or Systems; demonstrated potential for excellence in teaching; and a commitment to promoting diversity through their academic and research programs. Candidates must also demonstrate a commitment to graduate education. Equivalent foreign degrees are acceptable.

PREFERRED QUALIFICATIONS

Preferred candidates will possess an outstanding record of scholarship and research contributions in Computer Science; a record of excellence in teaching; the ability to effectively communicate with students in both large and small audiences, and a record of public engagement.

APPOINTMENT TERMS

This is a full-time, 9-month tenure track position.  Employment is conditional upon the timely completion of an approved I-9 (Employment Eligibility Verification Form). Candidates are expected to begin work on August 23, 2019. Salary will be commensurate with qualifications. The successful candidate’s academic appointment will be at the Storrs Campus.

TO APPLY

Visit Academic Jobs Online at https://academcjobsonline.org/ to complete your application. Please submit the following: a cover letter, curriculum vitae, research and scholarship statement, teaching statement (including teaching philosophy, teaching experience, commitment to effective learning, concepts for new course development, etc.); commitment to diversity statement (including broadening participation, integrating multicultural experiences in instruction and research and pedagogical techniques to meet the needs of diverse learning styles, etc.); and sample articles or books.  Additionally, please follow the instructions in Academic Jobs Online to direct three reference writers to submit letters of reference on your behalf.  Screening of applicants will begin immediately and continue until the position is filled.

Employment of the successful candidate will be contingent upon the successful completion of a pre-employment criminal background check.  (Search # 2019099)

This position will be filled subject to budgetary approval.

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 community. As an Affirmative Action/Equal Employment Opportunity employer, UConn encourages applications from women, veterans, people with disabilities, and members of traditionally underrepresented populations.


Application Materials Required:
Submit the following items online at this website to complete your application:
And anything else requested in the position description.

Further Info:
http://www.cse.uconn.edu/
 
Computer Science & Engineering Department
371 Fairfield Way, Unit 4155
University of Connecticut
Storrs, CT 06269-4155
Phone: (860) 486-3719
Fax: (860) 486-4817

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