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, algorithms, and programming. Knowledge and experience in artificial intelligence and machine learning is expected, but not required. Knowledge and experience in deep learning and generative AI is considered
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years, with a focus on creating an inclusive and bottom-up driven research environment. Our workplace consists of a diverse set of people from different nationalities, backgrounds and fields. As a PhD
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | about 2 months ago
://www.cmap.polytechnique.fr/~aymeric.dieuleveut/ ), professor at Ecole Polytechnique (Palaiseau). The successful candidate will implement and compare different distributed numerical optimization paradigms, that include
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is not a standalone concept and has close connections to diversity, transparency and bias. In this position, the PhD candidate will work on algorithmic fairness in job recommender systems
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selectivity is the first important barrier to overcome in order to perform quantitative analyses for each pollutant and avoid ionic interference between the different sensors used in the project. Sensor
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the improvement of the wetting/during algorithm in TELEMAC2D, including the effects of vegetation. Modelling the SPM turbidity in 3D (using TELEMAC3D) in front of the Belgian coast, validated with 3D remote sensing
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learning algorithms, and design of optical communication networks or power consumption and energy saving. The synergies of MATCH consortium act together to enable the thirteen DCs to become the next
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advantages. We will provide the necessary hardware and software for the real-time control of the machine, but the candidate will be responsible for developing and implementing the control algorithms. A working
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supervised deep learning algorithms for 3D laser data from forests Developing self-supervised deep learning algorithms for 3D laser data from forests Expand for a wider variety of downstream tasks focused
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this PhD project, you will investigate the co-design between event-based learning algorithms and neuronal hardware units with multi-scale time constants. The algorithmic methodology will exploit recent