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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 20 hours ago
of fitness landscapes and PLO structures designing new tunneling operators and search mechanisms implementing and experimentally evaluating new optimization algorithms designing computational experiments and
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 20 hours ago
scalability on high-performance computing (HPC) platforms. The main assignment will be to investigate how knowledge about the structure and local interactions of combinatorial optimization problems can be
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to develop, optimize and apply advanced mass spectrometry approaches for the structural and molecular characterization of ARDCs, and to analyze and interpret the resulting data. - Develop and optimize mass
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 20 hours ago
for final-year Master’s students or engineering school students with an interest in combinatorial optimization, constraint programming, and stochastic (hyper-)heuristics. The project will be carried out
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 20 hours ago
-adaptive systems. We focus particularly on two properties: self-healing and self-optimization. With self-healing, we aim to study and adapt data mining and machine learning solutions to the design and
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | about 20 hours ago
and constraints. It then necessitates a careful and meticulous work to delineate tasks and reorganize data structures, to maximize the optimization potential of the runtime system, while rigorously
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investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable states, attractors
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of studying and optimizing the microstructural and mechanical properties of granular materials bonded by a solidified foam, within the framework of the ANR project BONDINGFOAM. This mission is structured around
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-V’s, the identification of optimal conditions for minimizing defects, and the optimization of strain distribution, paving the way for more stable and high-performance quantum devices. Structural
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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