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. Inference methods will be developed within a maximum likelihood framework, based on Newton–Raphson-type optimization algorithms. Particular attention will be paid to the structural constraints imposed by sum
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | about 1 month ago
preserved at ambient temperature provided it is protected from light and humidity. Goal The goal of the project is to develop an algorithm to allow robust retrieval of data in the context of DNA-based data
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consensus. The successful candidate will develop the algorithmic foundations of this framework—making the motion-generation stack modular and heterogeneous (combining model-based control with learning-based
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19 Sep 2026 Job Information Organisation/Company CNRS Department Pouvoirs, Histoire, Esclavages, Environnement Atlantique Caraïbe Research Field Computer science Mathematics » Algorithms Researcher
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help reduce uncertainties in climate forecasts and better assess the effects of aerosols on atmospheric systems. The postdoctoral researcher's mission will be to develop and improve the WRF-Chem model
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on signals and complex systems. Gipsa-lab develops projects in the strategic fields of energy, the environment, communication, intelligent systems, life and health technologies, and language engineering
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
control applications. 2. Control Design nonlinear optimal control algorithms. Investigate advanced control strategies for compliant robotic manipulation. 3. Experimental Validation Validate the developed
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earthquakes. For fundamental reasons, estimations from seismic waves saturate for the largest events, which results in dramatic underestimation of the subsequent tsunamis. We have developed in our group a deep
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, optimal transport, set evolution and shape optimization, calculus of variations), and/or convex/non-convex optimization methods (first-order algorithms, nonlinear/non-Euclidean gradient descents
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essential for learning complex distributions over structured data such as text, graphs, and biological sequences. Developing and understanding models for dis- crete spaces is therefore a key challenge in