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) to develop accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models
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locomotion. Key Responsibilities: The successful candidate will: develop locomotion and local motion control algorithms for humanoid robots, implement learning-based and/or model-based control methods (e.g
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on designing and evaluating radar signal processing algorithms, developing sensing architectures, and advancing radar technologies through applied research, development, and evaluation. The successful candidate
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exploration and relevant disciplinaries Key Responsibilities: Develop novel imaging algorithms and validate them through synthetic experiments Extend these methods to real-world experiments to address practical
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algorithms and system frameworks that optimize cost, performance, and scalability. The role focuses on leveraging machine learning and reinforcement learning to enhance storage and service efficiency under
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tools (e.g. Power BI, Tableau or Qlik Sense) Experience in applying machine learning techniques and designing algorithms that are scalable and production-grade. Knowledge of database, ETL and data API
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digitization of object geometries, surface characteristics, and material properties. While conventional pipelines rely on generalized camera models, standard calibration targets, and associated algorithms, our
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approach makes it easier to identify different local optima using sampling mechanisms. In stochastic optimization, distribution estimation algorithms (EDA) are an alternative approach to traditional
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16 Apr 2026 Job Information Organisation/Company CNRS Department Laboratoire d'Informatique de Paris-Nord Research Field Computer science Mathematics » Algorithms Researcher Profile First Stage
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solutions that support research into the vulnerabilities of AI and ML algorithms and securing against those vulnerabilities. The Secure AI Lab within the SEI’s AI Division focuses on improving the security