Role overview
We're looking for early-career researchers and recent PhD graduates to join the team and contribute to the following research activities:
- Conduct research on machine learning approaches for sequential decision making and combinatorial optimization.
- Develop new models and algorithms, for example using deep reinforcement learning, graph neural networks, or other learning-based optimization techniques.
- Design and implement prototypes and proof-of-concept systems to evaluate new ideas and algorithmic approaches.
- Run and analyze large-scale experiments in simulation and realistic problem settings inspired by real robotic systems.
- Collaborate with researchers and engineers across NAVER LABS to transfer and demonstrate developed approaches on real robotic systems.
- Contribute to publications in leading conferences and journals in machine learning, artificial intelligence, optimization, and robotics.
What we're looking for
- Experience with deep reinforcement learning, in particular multi-agent reinforcement learning.
- Experience with machine learning for structured data, such as graph neural networks or related approaches.
- Experience with combinatorial optimization, neural combinatorial optimization, or learning-augmented optimization methods.
- Publications in leading conferences in machine learning, artificial intelligence, optimization, or robotics (e.g., NeurIPS, ICLR, ICML, AAAI, IROS, ICRA).
- Experience with large-scale experiments, simulation environments, or real-world-inspired problem settings.
- Interest in connecting machine learning research with real-world applications, in particular in robotics systems.
Tags & focus areas
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