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of synoptic processes can be isolated from those driven by surface conditions. Thanks to advances in ground-based remote sensing technology and algorithm development, those profile observations can now be
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control and energy management strategies, including centralized / distributed control approaches, for ESS coordination and ancillary service delivery. Develop optimization algorithms and Al-based methods
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-criteria, defining their formalization as fuzzy subsets, and characterizing their uncertainty; Integrating Machine Learning algorithms to better account for low-level sensor data (precipitation, wind-driven
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work will be organized around the following areas: 1. Bee detection and tracking: Development of computer vision algorithms to identify and track each bee from high-resolution images, while
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techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems
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(formulation, algorithms, applications in structural mechanics), HPC computing, reduced-order modelling, machine learning, Vibrations and structural dynamics, architected materials, Additive manufacturing
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/S0022112006003429 [2] A. Cahuzac, et al. “Smoothing algorithms for mean-flow extraction in large-eddy simulation of complex turbulent flows”, Physics of Fluids 1 December 2010; 22 (12): 125104, doi:10.1063/1.3490063
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interdisciplinary, and together we contribute to science and society. Your role Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms
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. The team's main research areas are: Architectures for Autonomous Robots, Learning, Temporal Planning and Execution Control, and Algorithmic motion planning. RIS is composed of 8 permanent researchers, 4
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on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate