Graduate Teaching Assistant (must hold a Diploma/Master's degree, Ph.D. position) in Robot Learning

The newly founded Institute for Machine Intelligence at Johannes Kepler University Linz studies how intelligent behavior emerges from learning systems interacting with the physical world. We develop algorithms and robotic platforms that enable machines to learn from experience, generalize beyond training conditions, and operate autonomously in complex environments to solve the toughest real-world challenges. We place special focus on the governing principles of the robotic body in conjunction with learning algorithms. Our ambition is to now develop methods that answer key questions within this framework. We are looking for an enthusiastic personality who wants to commit to our mission.

Position highlights:

• Individually tailored mentoring and career development: Your success is our priority! You will receive close, individualized mentoring tailored to your strengths and career goals.
• Build a strong international network: We have close ties to leading research groups worldwide, including the MPI for Intelligent Systems, TU Darmstadt, and the University of Alberta. Visits, collaborations, and internships in both academia and industry are actively encouraged.
• Shape your environment: As one of the first scientific members of our newly founded institute, you have the unique opportunity to help define its culture, research agenda, and identity from the very start.

Job Duties:

• Contribute to the institute's effort in research and teaching in the field of robot learning
• Your research will tackle some of the most exciting open problems in robot learning, including but not limited to: continual learning & lifelong adaptation, sim-to-real transfer, foundation models for embodied intelligence, soft robotics, reinforcement learning algorithms, whole-body control & loco-manipulation, and tactile sensing & contact-rich manipulation
• Your work will bridge simulation and the physical world: methods are first developed and tested in simulation, then deployed on physical robots: both commercial platforms and custom hardware developed within the institute
• Publish and present our scientific findings in world-wide, top-tier robotics and machine learning venues such as CoRL, ICRA, R:SS, NeurIPS, ICLR, ICML, TRO, TRL, IJRR, Science Robotics
• Supervise students and develop your skills as a mentor: learn to guide, inspire, and bring out the best in others under the guidance and support of the PI

Your Qualifications:

• Be a highly motivated, independent, and ambitious personality
• The successful candidate must hold a completed diploma or master's degree in, but not limited to, robotics, computer science, mechanical engineering, electrical engineering, machine learning/artificial intelligence (with lots of interest in robot hardware design), physics, or math
• Be thrilled about AI and robotics
• Experience with programming (Python, C++, or similar)
• Familiarity with simulation environments (e.g. MuJoCo, Isaac Gym/Sim), Robot Operating System (ROS) / ROS2, and deep learning frameworks (e.g. PyTorch) is a plus
• Willingness to acquire complementary disciplinary competencies required for this doctoral project
• Be able to communicate and write in English (C1) and German (B2)
• Ability to think analytically and communicate thoughts clearly, both verbally and in writing. Be accurate, reliable, creative, and resilient. Be comfortable with uncertainty and open-ended problems

Excited? We'd love to hear from you! Please upload the following documents:

• A personal statement that describes why you want to pursue a doctorate at the Institute for Machine Intelligence as well as what particular aspect of this research area interests you.
• A complete CV in English.
• Transcript of records and certificates from your university degrees (M.Sc. degree and B.Sc. degree).

Please note: If your personal statement appears to be primarily AI generated, your chances of advancing may not be very high. We want to get to know you and why and what you want to research!

Applications will be evaluated on a rolling basis, we encourage interested candidates to apply early. Shortlisted candidates will be invited for an interview.