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modeling, construction of numerical methods, coding, testing, numerical simulations, and possibly measurements. You will mainly do your programming work in a mixed programming environment, i.e. combining
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project focuses on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High
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to technologies optimization. The research team has the ambition to address all the needed scientific fields to understand turbulent and multiphase flows from simulation: numerical methods, turbulence models
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the position. Your work tasks You will develop and validate digital-twin and optimization methods for electrolysis systems, working both independently and collaboratively with the group and with academic and
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are looking for a postdoctoral researcher in computational methods for aeroacoustics and optimisaiton related to wind turbines. The successful candidates will join the dynamic research team of Prof. Esteban
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have access to state-of-the-art facilities, including cleanroom microfabrication, laser micromachining, 3D printing, advanced microscopy, and numerical modeling tools. The project benefits from a close
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, can be transferred to work directly on experimental measurements. In this PhD, you will develop AI-driven ultrasonic methods for quantitative materials evaluation, focusing on inverse models
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scientific hypotheses numerically further develop the theoretical framework of the project extend and develop methods to apply functional connectivity methods and causal frameworks to neuronal spiking data and
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modeling Demonstrated experience with mathematical modelling, and an ability to learn new concepts and (computational) methods as needed A demonstrated ability to work with programming languages such as R
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
(or surrogate models) are approximations of classical numerical solvers with a very low computational cost. They form the core of a digital twin. Using machine learning techniques to build these meta-models