Working Student - Reinforced Learning Environments (40% FTE+)
Daedalean AG
Zürich
Key information
- Publication date:21 July 2025
- Workload:40%
- Contract type:Unlimited employment
- Place of work:Zürich
Job summary
Daedalean is a Zürich-based startup aiming to revolutionize air travel. Join our innovative team focused on autonomy in flying vehicles.
Tasks
- Conduct research in reinforced learning for mission planning.
- Implement algorithms to enhance decision-making in simulations.
- Analyze results and document findings from experiments.
Skills
- Experience with Python, C++, or MATLAB is beneficial.
- Interest in AI and machine learning is essential.
- Studying Robotics, Computer Science, or a related field.
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About us:
Daedalean is a Zürich-based startup founded by experienced engineers who want to completely revolutionize air travel within the next decade. We combine computer vision, deep learning, and robotics to develop full “level-5” autonomy for flying vehicles.
Your role:
To conduct research and development in the field of reinforced learning for mission planning and optimization.
This is a position at at least 40% FTE and minimum of 6 months duration.
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- To research and develop reinforcement learning environments for mission planning and optimization.
- Implement reinforcement learning algorithms: Apply and test algorithms to improve decision-making within simulated environments.
- Conduct experiments and analyze results: Run simulations, collect data, and evaluate the effectiveness of different approaches.
- Document and share findings: Prepare clear documentation and reports on your work and results.
- Experience with Python, C++ or MATLAB is beneficial.
- Interest in AI and machine learning: advanced knowledge or coursework in reinforcement learning, machine learning, or related fields.
- Interest in computer gaming.
- Studying Robotics, Computer Science, or a related technical field.
- A team of experienced engineers and researchers, who joined us from most recognized companies and institutions.
- Difficult and interesting problems to solve.
- Hybrid work setting.
- Gym membership
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