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ones for different stakeholder groups, as a basis for policy making? Are you interested in spatial optimization algorithms and uncertainty assessment? Then this PhD position at the Department of Human
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interpretable framework for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 19 hours ago
frameworks, integrating frugal AI algorithms Participate to the experimenta validation of teh implemented solutions Write reports Test, change up until validation Where to apply Website https://jobs.inria.fr
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to develop and implement state estimation algorithms, such as Kalman filters and observers, for real-time applications. Proficiency in MATLAB and Simulink for modelling, simulation, and validation using
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conduct cutting-edge research on the interplay of theoretical computer science, mathematics, and quantum mechanics. Particular topics include classical and quantum aspects of: Sublinear algorithms and
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persist across participants, and retraining from scratch is often computationally infeasible at scale. In this project, you will develop the next generation of federated machine unlearning algorithms
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needed. This position will conduct cutting-edge research focused on developing algorithms and AI approaches for a wide array of research applications and tasks. This position will facilitate
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Research Infrastructure? No Offer Description The objective is to develop advanced algorithms for AI-enabled semantic and robot-control-aware communication with a particular focus on the management and
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these connected directions. Research will emphasise new learning algorithms and rigorous evaluation using public datasets, simulation and, where available, robotic and edge-computing platforms. Evaluation will
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The successful PhD candidate will undertake research in the following areas: Develop deep learning algorithms for autonomous robotic navigation using dual-view image fusion and shadow-based visual perception