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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 23 days ago
fields including health, agriculture and ecology, sustainable development. More information, please visit https://team.inria.fr/scool/projects Odalric-Ambrym Maillard is a permanent researcher at Inria. He
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contexts, including the use of surrogate models. Where to apply Website https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079 Requirements Additional Information Eligibility criteria
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sources. Where to apply Website https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079 Requirements Additional Information Eligibility criteria Oral test aimed at ascertaining candidates
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data. The work includes also the implementation of calibration and control algorithms for automated configuration and stabilization of the operating point of active photonic components. Where to apply
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of signal processing and machine learning algorithms for the extraction of acoustic, prosodic, and semantic parameters from voice recordings. Alongside the innovative research activities, the project requires
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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The project consists in the development algorithms for the indirect estimation
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The Marketing group at Rotterdam School of Management, Erasmus University seeks a highly motivated PhD student with strong quantitative skills to study the problem of algorithmic biases in marketing. As machines
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algorithms Predicting structured output Self-supervised learning Computational metabolomics Computational biomedicine Computational drug discovery Excellent technical and communications skills are required
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | about 4 hours ago
questions in Earth System science is also a goal of this opportunity. Efforts are expected to be conducted in collaboration with the SAGE algorithm/data processing and validation teams, as
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis