Research Associate
vor 2 Wochen
Job Summary
We are seeking a highly skilled Research Associate to join our team at Universität Hamburg. The successful candidate will be responsible for conducting research and teaching in the field of multimodal Large Language Models and human robot interaction.
Key Responsibilities
- Conduct research on multimodal Large Language Models to guide manipulation, sensors, and perception
- Develop and implement new methodology for human robot interaction systems
- Teach and conduct research in the field of intelligent robotics and multimodal Large Language Models
- Pursue further academic qualifications, including a doctoral dissertation
Requirements
- University degree in a relevant field
- At least a Master, Diploma, or equivalent degree in Computer Science
- Extensive programming knowledge in ROS (C++, C, Python), and model training frameworks (e.g., Pytorch, Keras, Tensorflow)
- Practical experience with NVIDIA clusters of GPUs (e.g., H100)
- Good knowledge of English (written and oral)
- Ability to work in a team and confident presentation skills
What We Offer
- Reliable remuneration based on wage agreements
- Continuing education opportunities
- University pensions
- Attractive location
- Flexible working hours
- Work-life balance opportunities
- Health management
- Educational leave
- 30 days of vacation per annum
About Universität Hamburg
Universität Hamburg is one of the strongest research educational institutions in Germany. Our work in research, teaching, educational, and knowledge exchange activities is fostering the next generation of responsible global citizens ready to tackle the global challenges facing us. Our guiding principle "Innovating and Cooperating for a Sustainable Future" drives collaboration with academic and non-academic partner institutions in the Hamburg Metropolitan Region and around the world.
Equal Opportunities
We promote equal opportunity and encourage women to apply for this position. Equally qualified and suitable female applicants will receive preference. Severely disabled and disabled applicants with the same status will receive preference over equally qualified non-disabled applicants.
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