Open Rank - Assistant or Associate Professor
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Posting Details
Posting Details
Position Number: | 128764 |
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Title: | Open Rank |
Functional Title: | Open Rank - Assistant or Associate Professor |
Category Status: | 02-Faculty Non-Tenured, On Track |
Applicant Search Category: | Faculty |
University Authorized FTE: | 100.0 |
Unit: | BMGT-Decision, Operations & Information Technologies |
Campus/College Information: | Founded in 1856, University of Maryland, College Park is the state’s flagship institution. Our 1,250-acre College Park campus is just minutes away from Washington, D.C., and the nexus of the nation’s legislative, executive, and judicial centers of power. This unique proximity to business and technology leaders, federal departments and agencies, and a myriad of research entities, embassies, think tanks, cultural centers, and non-profit organizations is simply unparalleled. Synergistic opportunities for our faculty and students abound and are virtually limitless in the nation’s capital and surrounding areas. The University is committed to attracting and retaining outstanding and diverse faculty and staff that will enhance our stature of preeminence in our three missions of teaching, scholarship, and full engagement in our community, the state of Maryland, and in the world. |
Background Checks | Offers of employment are contingent on completion of a background check. Information reported by the background check will not automatically disqualify you from employment. |
Position Summary/Purpose of Position: | The Decision, Operations and Information Technologies (DO&IT) Department at The Robert H. Smith School of Business, University of Maryland, College Park, MD invites applications for a tenure-track faculty position at the Assistant or Associate Professor level in Business Analytics, starting Fall 2024. We are seeking candidates with a strong commitment to high-quality research, and for candidates at the Associate Professor level, a commensurate record of research publications. We also expect the faculty member to play a key role in the Department’s academic programs at the Doctoral, Master’s and Undergraduate levels, in terms of teaching and leadership. |
Benefits Summary | Top Benefits and Perks: |
Minimum Qualifications: | The ideal candidate will have a PhD in Business Analytics, Operations Management, Management Science, Industrial Engineering/Operations Research, Statistics, Computer Science, or a related field, and is well trained in data science and machine learning. We seek candidates who use data as a core element of their research and have a proven track record (for the Associate Professor rank) or a demonstrated interest (for the Assistant Professor rank) in one of the Smith School’s strategic focus areas including: Artificial intelligence (AI) applications in operations management and business analytics; algorithmic bias and responsible AI; future of supply chains; sustainability and climate risk; environmental, social and governance (ESG) issues; applications that support diversity, equity and inclusion (DEI). The faculty member is expected to develop and teach analytics related courses to undergraduates, MS, MBA and / or PhD students. |
Preferences: | |
Additional Certifications: | |
Additional Information: | The DO&IT faculty at the University of Maryland (http://www.rhsmith.umd.edu/doit/) is a vibrant and interdisciplinary group actively engaged in theoretical and applied research spanning business analytics, operations management, information systems, management science, and statistics. Several faculty members hold joint or affiliate appointments across the university in mathematics, engineering, computer science. The Smith School is a recognized leader in management research and education, with nationally ranked MBA, MS and undergraduate programs, as well as a strong doctoral program. The school is consistently ranked among the top business schools by leading publications such as U.S. News & World Report, Financial Times, Business Week, and Wall Street Journal. Interested applicants are asked to submit their entire application electronically as one PDF file as an email attachment to the Search Committee at analyticsfacultysearch@umd.edu. Applicants must also submit their application to the University of Maryland’s official jobs website: https://ejobs.umd.edu (search for position number 128764). The application file should include a brief cover letter, vita, research and teaching statements, and names of three references, in that order. The file should be named with the candidate’s last name and first name, e.g., SmithJohn. For full consideration, applications are due by November 26, 2023. Applications will continue to be accepted until the position is filled. Any questions regarding the position or application process can be directed to the Search Committee at analyticsfacultysearch@umd.edu. |
Posting Date: | 09/12/2023 |
Closing Date: | |
Open Until Filled | Yes |
Best Consideration Date | 11/26/2023 |
Physical Demands | |
Diversity Statement: | The University of Maryland, College Park, an equal opportunity/affirmative action employer, complies with all applicable federal and state laws and regulations regarding nondiscrimination and affirmative action; all qualified applicants will receive consideration for employment. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, religion, sex, national origin, physical or mental disability, protected veteran status, age, gender identity or expression, sexual orientation, creed, marital status, political affiliation, personal appearance, or on the basis of rights secured by the First Amendment, in all aspects of employment, educational programs and activities, and admissions. |
Applicant Documents
Required Documents
- Cover Letter
- Curriculum Vitae
- Research (examples: research statement, research programs)
- Teaching (examples: teaching philosophy, student evaluations)
- List of References (no emails sent from system)
Posting Specific Questions
Required fields are indicated with an asterisk (*).
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