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multimodal data streams combining video, metadata derived from artificial intelligence algorithms, and information collected from surrounding objects. These data streams exhibit heterogeneous constraints in
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, but this option can be time consuming and unsafe when testing complex algorithms (navigation, grasping . . . ). Using a simulator can speed up development, is safe, but breaks the natural interaction
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the standardization of acquisitions, Developing deep learning algorithms to define and learn relevant spectral signatures and link them to clinical phenotypes, Evaluating and validating the platform on representative
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the delivery of wider co-benefits and minimizing costs. The proposed multi-objective approach, based on optimization and machine learning algorithms, is used for defining optimal configurations, including water
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) • Execution of a reduced set of robotic gestures to validate the choice of formalism (M8) • Proposal of algorithms to deal with variability (of utensils, ingredients) for task planning and execution (M16
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multimedia networks. IEEE Communications Magazine, 2013, vol. 51, no 6, p. 152-159. Work Plan Defence and Space, combining theoretical research, algorithm design, simulation, and experimental validation
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The candidate must have a strong background in algorithmics, optimization, and data science. In particular: Mathematical modelling: ability to formalize real-world personnel scheduling problems, including
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environment (real testbed or emulation) to implement attack scenarios and measure their impact on service availability. You will design and validate real-time attack detection algorithms capable of meeting the