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About the Role
This postdoctoral fellow position will perform research within the Center for Generative Artificial Intelligence and its parent organization, the Machine Learning Laboratory. Successful candidates will be appointed within the Department of Computer Science, the Department of Electrical and Computer Engineering, or the College of Information. The fellowship includes funding for independent travel to workshops, conferences, and other universities and research labs.
Responsibilities
- Read and write manuscripts for publication in peer-reviewed conferences and journals.
- Present research findings in public forums.
- Collaborate with researchers and students at UT Austin.
- Organize seminars, workshops, and other research activities.
- Advance collaborative projects with industry via the Machine Learning Laboratory’s industry affiliates partner program.
- Spend one day per week on industry-facing projects.
Requirements
- Ph.D. earned within the last three years in the field of computer science or engineering.
- Productive, self-driven, and collaborative scholar with a strong publication track record.
- Good communication skills and ability to convey research ideas to a broad audience.
Location
- AUSTIN, TX
Work Type
- Full-time
Experience Level
- Postdoctoral
Education Level
- Ph.D.
Salary/Compensations
- $80,000+
Benefits
- Competitive salary and benefits
- Funding for independent travel to workshops, conferences and other universities and research labs.
About the Company
- The University of Texas at Austin is an equal opportunity/affirmative action employer.
Equal Opportunity
- The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.