Postdoctoral Fellow
Job in
Austin, Travis County, Texas, 78701, USA
Listed on 2026-06-27
Listing for:
The University of Texas at Austin Staff
Full Time
position Listed on 2026-06-27
Job specializations:
-
Research/Development
Research Scientist, Robotics, Postdoctoral Research Fellow
Job Description & How to Apply Below
Postdoctoral Fellow
This postdoctoral fellow position reports to Dr. Yuke Zhu in the College of Natural Science - Department of Computer Science to perform ongoing research in the Robot Perception and Learning (RPL) Lab.
Responsibilities:
- Develop new AI algorithms and Robotics systems and conduct empirical studies at scale.
- Initiate, plan, execute and evaluate experiments to achieve specific objectives in research projects.
- Read and write manuscripts/review articles for publication in peer-reviewed conferences and journals.
- Present research data and findings in public and private forums (seminars/laboratory meetings).
- Collaborate with researchers and students at UT Austin
- Ph.D. earned within the last three years in the fields of Artificial Intelligence and/or Robotics;
- Excellent technical abilities and programming skills;
- Productive, self-driven, and collaborative scholar with a strong publication track record;
- Strong communication skills and ability to convey research ideas to a broad audience.
- Knowledge of robotics simulation and foundation models;
- Experience working with physical robot hardware.
- Experience working with large-scale machine learning systems;
- Experience mentoring students and/or leading project teams of 3+ researchers.
$60,000+ depending on qualifications
Working Conditions- May work around standard office conditions;
- May work in an academic research environment;
- May work with large robotic systems or autonomous vehicles;
- Occasional work around electrical and mechanical hazards;
- Repetitive use of a keyboard at a workstation.
- Cover letter (up to two pages),
- Curriculum vitae,
- Names and contact information of 3-5 references,
- Electronic copies of two most representative publications.
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