Working Student - Reinforced Learning Environments (40% FTE+)

Daedalean, startup basée à Zürich, révolutionne les voyages aériens. Rejoignez une équipe innovante pour transformer l'avenir de l'aviation.

Tâches

• Mener des recherches en apprentissage renforcé pour l'optimisation.

• Implémenter des algorithmes d'apprentissage pour des décisions améliorées.

• Documenter et partager les résultats des expériences menées.

Compétences

• Expérience en Python, C++ ou MATLAB et intérêt pour l'IA.

• Connaissances en apprentissage machine et robotique.

• Capacité à résoudre des problèmes complexes en équipe.

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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Your tasks will include the following: • 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.

Qualification and Experience: • 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.

Benefits: • 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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