Closing Date:
Type of Position:
Graduate Assistant
Workstudy Position:
No
Job Type:
Academic Term (Fixed Term)
Work Shift:
Sponsorship Available:
No
Institution Name:
University of Arkansas at Little Rock
The University of Arkansas at Little Rock is a metropolitan research university that provides an accessible, quality education through flexible learning and unparalleled internship opportunities. At UA Little Rock, we prepare our more than 8,900 students to be innovators and responsible leaders in their fields. Committed to its metropolitan research university mission, UA Little Rock is a driving force in Little Rock's thriving cultural community and a major component of the city and state's growing profile as a regional leader in research, technology transfer, economic development, and job creation.
Department:
COSMOS - Provisional Positions
Department's Website:
https://cosmos.ualr.edu/
Summary of Job Duties
The Graduate Research Assistant will develop machine learning and artificial intelligence (ML/AI)-driven socio-computational models to analyze social media data, particularly video-based social media data. Analysis will include (but not limited to) content analysis and network analysis. Content will include text, multimedia, and metadata. The Graduate Research Assistant will work with the research team to develop predictive models of socio-technical behaviors and identify key actors and key groups responsible for coordinating cyber campaigns. This is an on-campus position. Position reports to Dr. Nitin Agarwal (nxagarwal@ualr.edu), Maulden-Entergy Endowed Chair and Distinguished Professor and Director, COSMOS Research Center, UA-Little Rock. This position is governed by state and federal laws, and agency/institution policy.
Qualifications
Required Education and/or Experience:
- Bachelor's Degree from an accredited institution of higher education;
- Regular admission to a graduate degree program or good standing with the Graduate School for continuing graduate students;
- Ability to work the required hours (FT-50% - 20 hours per week/PT-25% - 10 hours per week) with regular attendance.
Preferred Education and/or Experience:
- The candidate must have a bachelor's degree in Computer Science, Information Science, or a related discipline;
- A candidate with experience in computer programming and data management will be given preference;
- Experience with graph theory and graphic libraries such as Gephi or NetworkX.;
- Experience with crawling web data;
- Familiar with UNIX-based systems such as Linux.
Job Duties and Responsibilities
- Conduct research applying machine learning and AI methods to social media data;
- Assist with video-based online information environment analysis;
- Assist with data collection, cleaning, preprocessing, analyzing findings from the data, writing research findings, and presenting the research at conferences, scientific venues, program reviews, and other venues;
- Assist in preparing reports and publications;
- Coordinate meetings and lead discussions;
- Other tasks as needed.
Knowledge, Skills, and Abilities
- Extensive knowledge of R or Python, including data analysis and graphing libraries such as Pandas, Matplotlib, Plotly, as well as ML libraries such as SK-Learn, TensorFlow;
- Experience with machine learning techniques and models such as classifiers, transformers, clustering, etc.;
- Knowledge of DB systems and SQL language;
- Knowledge of computational social science and social network analysis;
- Ability to interact with people of diverse ethnic backgrounds;
- Communication skills, writing skills, and public speaking;
- Effective organizational skills.
Salary Information:
$11,250-$13,500
Required Documents to Apply:
Resume
Recruitment Contact Information:
Prof. Nitin Agarwal, Maulden-Entergy Endowed Chair, Donaghey Distinguished Professor, and Director, COSMOS Research Center at UA-Little Rock.
nxagarwal@ualr.edu
All application materials must be uploaded to the University of Arkansas System Career Site https://uasys.wd5.myworkdayjobs.com/UASYS
Please do not send to listed recruitment contact.
Pre-employment Screening Requirements:
No Background Check Required
The University of Arkansas at Little Rock is committed to providing a safe campus community. We conduct background checks for applicants being considered for employment. Background checks include a criminal background check and a sex offender registry check. For certain positions, there may also be a financial (credit) background check, a Motor Vehicle Registry (MVR) check, and/or drug screening. Required checks are identified in the position listing. A criminal conviction or arrest pending adjudication or adverse financial history information alone shall not disqualify an applicant in the absence of a relationship to the requirements of the position. Background check information will be used in a confidential, non-discriminatory manner consistent with state and federal law.
The University of Arkansas is an equal opportunity institution. The University does not discriminate in its education programs or activities (including in admission and employment) on the basis of any category or status protected by law, including age, race, color, national origin, disability, religion, protected veteran status, military service, genetic information, sex, sexual orientation, or pregnancy. Questions or concerns about the application of Title IX, which prohibits discrimination on the basis of sex, may be sent to the University's Title IX Coordinator and to the U.S. Department of Education Office for Civil Rights.
Persons must have proof of legal authority to work in the United States on the first day of employment.
All application information is subject to public disclosure under the Arkansas Freedom of Information Act.
Constant Physical Activity:
Manipulate items with fingers, including keyboarding, Repetitive Motion, Sitting, Standing, Talking, Walking
Frequent Physical Activity:
Manipulate items with fingers, including keyboarding, Repetitive Motion, Sitting, Standing, Talking, Walking
Occasional Physical Activity:
Manipulate items with fingers, including keyboarding, Repetitive Motion, Sitting, Standing, Talking, Walking
Benefits Eligible:
No
