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, data scientific or machine learning background that is keen to work in an interdisciplinary environment and open to collaborating with researchers from other disciplines. The successful candidate will
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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Mines Paris - PSL, Centre PERSEE | Sophia Antipolis, Provence Alpes Cote d Azur | France | about 2 months ago
-focused learning" or "End-to-end learning". For example, end-to-end machine learning (ML) models can be trained to minimize the downstream decisions regret or even directly learn a mapping from data to
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problems. This level of complexity increases when considering the multi-period operation of the system. These are difficult to solve using traditional strategies, so in recent years machine learning
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strong interest in computer science (software development, machine learning techniques, etc.) is desirable. · Applicants must have a maximum of 3 years of research experience after the PhD. · Language
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the migration of Li from source to deposit. Published data containing information on Li partitioning between liquid and solid phases will be used to derive simplified chemical laws through machine learning
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 3 months ago
has been driving computer performance for decades through CMOS down-scaling and architecture enhancements, resulting in doubled performance every 18 months. However, current technology encounters three
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 3 months ago
the complexity and capabilities of Machine Learning (ML) models have made Artificial Intelligence (AI) able to tackle challenges ranging from vision and graphics to natural language, and even creative tasks
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/technical challenges Project FITNESS will build upon and extend state-of-the-art methods [1], [2] recently developed within the team, showing to outperform existing, machine-learning based approaches in
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into clinical neonatology ensures direct medical applicability, fostering collaboration between engineers, computer scientists, and healthcare professionals. 1.5. References [Andrychowicz2016] Andrychowicz, M