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Zouitine Mehdi

@zouitinemehdi

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PhD-level AI researcher focused on reinforcement learning and operations research for real-world systems.

France

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What I'm looking for

I am seeking a role applying reinforcement learning and operations research to impactful, measurable real-world problems, with opportunities for research-to-deployment work and cross-disciplinary collaboration.

I am a PhD in AI with six years of experience applying machine learning and operations research to real-world problems across defense, aerospace, and robotics. I have authored peer-reviewed papers at NeurIPS, CPAIOR and LION, and I drive projects from concept to deployment.

My work spans routing, scheduling, and combinatorial optimization, with applied contributions in satellite constellation optimization, delivery routing, multi-UAV control, and computer vision for Earth observation and medical applications. I have presented at major workshops and competitions and regularly participate in AI challenges.

I seek impactful opportunities where reinforcement learning and operations research solve measurable, real-world problems, bringing rigorous research, practical engineering, and a results-oriented mindset to cross-disciplinary teams.

Experience

Work history, roles, and key accomplishments

Current

AI Research Scientist

Current

Naver Labs Europe

Jan 2025 - Present (1 year)

Develop RL and operations research algorithms for real-world robotics routing and delivery applications, including deployment work for Naver 1789 robotic building systems.

Reinforcement Learning Operations Research Routing Robotics Python Simulation

Research Engineer

IRT Saint Exupéry

Jan 2021 - Jan 2025 (4 years)

Applied machine learning and operations research to Earth observation projects, including CV for remote sensing and scheduling optimization; developed learning-based combinatorial optimization methods during PhD work.

Machine Learning Operations Research Computer Vision Scheduling Combinatorial Optimization Python Deep Learning Satellite Applications

Intern — Flight Test

Airbus

Jan 2020 - Jan 2021 (1 year)

Built deep learning models for unsupervised anomaly detection in flight test data and developed sensor surrogate neural models to replace costly sensors and reduce testing costs.

Deep Learning Anomaly Detection Siamese Networks Sensor Modeling Python Data Analysis Model Evaluation

Education

Degrees, certifications, and relevant coursework

Toulouse Mathematics Institute

Doctor of Philosophy, Reinforcement Learning / Operations Research

2022 - 2025

Activities and societies: Authored peer-reviewed papers (NeurIPS, CPAIOR, LION); research projects in routing and scheduling; presentations at workshops and conferences.

PhD in Reinforcement Learning focusing on neural combinatorial optimization and learning-based algorithms for real-world optimization problems such as satellite constellation optimization.