Senior Research Scientist - Machine Learning for Decision Making and Optimization F/M

We're looking for an experienced researcher to join the team and contribute to the following research activities:

• Lead research projects on machine learning approaches for sequential decision making and combinatorial optimization.
• Identify and formulate impactful research problems inspired by real-world robotics applications.
• Develop new models and algorithms, for example using deep reinforcement learning, graph neural networks, or other learning-based optimization techniques.
• Design and oversee the implementation of prototypes and proof-of-concept systems to evaluate new approaches.
• Play a leading role in communicating research results, including publications in top-tier conferences and journals.
• Collaborate with researchers and engineers across NAVER LABS to transfer and demonstrate developed approaches on real robotic systems.
• Mentor and collaborate with junior researchers and research engineers.
• Contribute to the visibility of the team through publications, talks, and collaborations.

• PhD in machine learning, optimization, robotics, computer science, or a related field.
• Strong research track record in machine learning, artificial intelligence, optimization, or related areas, demonstrated for example through publications in leading conferences or journals.
• Strong background in machine learning and/or sequential decision making.
• Proven ability to lead and drive research projects independently.
• Excellent programming skills in Python, and experience with deep learning frameworks such as PyTorch.
• Experience in designing, implementing, and evaluating machine learning models.
• Ability to mentor and collaborate with junior researchers and work effectively in multidisciplinary research environments., * 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.
• Demonstrated ability to identify and formulate impactful research problems.
• Experience mentoring students, interns, or junior researchers.
• Strong publication record in leading conferences in machine learning, artificial intelligence, optimization, or robotics (e.g., NeurIPS, ICLR, ICML, AAAI, IROS, ICRA).
• Experience building collaborations across teams or disciplines.
• Interest in connecting machine learning research with real-world applications, in particular in robotics systems.

The team regularly publishes in leading AI and robotics conferences. Recent publications include:

• Learning to Solve the Multi-Agent Task Assignment Problem for Automated Data Centers - IROS 2025

• GOAL: a Generalist Combinatorial Optimization Agent Learner - ICLR 2025

• Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses - ECAI 2024

• BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization - NeurIPS 2023

• We foster a collaborative environment dedicated to ambitious, multidisciplinary projects that translate advanced research into impactful, real-world solutions, supported by 30+ years of experience in AI and related fields.

• Flexible work/life balance.

• We are an equal opportunity employer that hires based on skills, experience, and merit. We foster an inclusive and diverse workplace where all qualified candidates are considered fairly, regardless of background.

• We're based in Meylan, close to Grenoble, a city that offers the perfect balance of urban life, cutting-edge research and technology, and spectacular mountain landscapes that provide countless opportunities to relax, recharge, and enjoy the outdoors.

NAVER LABS Europe is part of the R&D division of NAVER, Korea's leading Internet portal and a global tech company with a range of services that include search, commerce, content, fintech, robotics and cloud. About the team The Optimization with Learning team at NAVER LABS Europe conducts research at the intersection of machine learning and mathematical optimization, with a focus on sequential decision making, combinatorial optimization, and multi-agent coordination. Our work is motivated by challenging real-world robotics problems, in particular large-scale robot fleet coordination tasks in uncertain and dynamic environments. Our goal is to develop principled learning-based approaches for sequential decision making and combinatorial optimization that generalize beyond a single application and advance the broader research field. Through close collaborations with robotics teams across NAVER LABS, researchers have the opportunity to connect fundamental research questions with real operational problems. This creates a unique environment to pursue ambitious research directions, publish in leading conferences, and contribute to emerging AI-driven robotics systems. Senior researchers in the team play a key role in shaping research directions, leading projects, and mentoring junior researchers., NAVER LABS, co-located in Korea and France, is the organization dedicated to preparing NAVER's future. Scientists at NAVER LABS Europe are empowered to pursue long-term research problems that, if successful, can have significant impact and transform NAVER. We take our ideas as far as research can to create the best technology of its kind. Active participation in the academic community and collaborations with world-class public research groups are, among others, important ways to achieve these goals. Teamwork, focus and persistence are important values for us.