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Assistant Professor - Data-driven Modeling and Simulation of Complex Systems
Assistant Professor - Data-driven Modeling and Simulation of Complex SystemsInsideHigherEd • Columbia, South Carolina, United States
Assistant Professor - Data-driven Modeling and Simulation of Complex Systems

Assistant Professor - Data-driven Modeling and Simulation of Complex Systems

InsideHigherEd • Columbia, South Carolina, United States
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Logo : Posting Number :

FAC00179PO23

Advertised Title :

Assistant Professor - Data-driven Modeling and Simulation of Complex Systems

Campus : Columbia

College / Division :

College of Arts and Sciences

Department : CAS Mathematics

Advertised Salary Range :

We offer a competitive salary and benefits package.

Part / Full Time : Full Time

Hours per Week : 40

Position Category :

Full-time Equivalent (FTE)

Basis : 9 months

Work County : Richland

Tenure Information :

Tenure-Track / Tenured

Job Search Category :

Faculty

About University of South Carolina :

From the Upstate to the Lowcountry, the University of South Carolina system is transforming the lives of South Carolinians through the impact of our eight institutions and 20 locations throughout the state. More than 50,000 students are enrolled at one of eight institutions, including the research campus in Columbia and comprehensive four-year universities in Aiken, Upstate and Beaufort. In addition, our Palmetto College campuses in Salkehatchie, Union, Lancaster and Sumter enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build healthier, more educated communities in South Carolina and beyond.

Advertised Job Summary :

The Department of Mathematics at the University of South Carolina is inviting applications for two 9-month tenure track Assistant Professor positions in data-driven modeling and simulation of complex systems. The candidate is expected to contribute to synergistically bridging important developments in emerging mathematical foundations in Data Science and Artificial Intelligence and numerical simulation of complex systems. A successful candidate is expected to teach at all levels in the Department of Mathematics, and to participate actively in the state-wide NSF-funded EPSCOR project on AI-enabled Devices for the Advancement of Personalized and Transformative Healthcare in South Carolina.

Required Education and Experience :

Candidates must have a Ph.D. in Mathematics by the start date of employment, an outstanding research record, a commitment to effective teaching at the undergraduate and graduate levels, and a demonstrated potential for excellence in both research and teaching.

Preferred Qualifications :

Ph.D. in Mathematics

Desired Start Date : 08 / 16 / 2024

Job Open Date : 10 / 03 / 2023

Open Until Filled : Yes

Special Instructions to Applicant :

All applicants must apply at USCJobs at (https : / / uscjobs.sc.edu) as well as via MathJobs at (https : / / www.mathjobs.org). A completed application will include a cover letter, curriculum vitae, description of research plans, and statement of teaching philosophy. In addition, four letters of recommendation should be made available through MathJobs. One of the letters should appraise the candidate’s teaching ability.

Positions are advertised for a minimum of five (5) business days on our job website. After five (5) business days, positions can be closed at the discretion of the department at any time. This position is open until filled. This employment site is updated on a regular basis. The length of the recruitment and screening process may vary from position to position, depending upon a variety of factors. Should review of your qualifications result in a decision to pursue your candidacy, you will be contacted by phone or email.

Quicklink for Posting :

https : / / uscjobs.sc.edu / postings / 154173

EEO Statement :

The University of South Carolina does not discriminate in educational or employment opportunities or decisions for qualified persons on the basis of age, ancestry, citizenship status, color, disability, ethnicity, familial status, gender (including transgender), gender identity or expression, genetic information, HIV / AIDs status, military status, national origin, pregnancy (false pregnancy, termination of pregnancy, childbirth, recovery therefrom or related medical conditions, breastfeeding), race, religion (including religious dress and grooming practices), sex, sexual orientation, veteran status, or any other bases under federal, state, local law, or regulations.

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