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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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» Other Engineering » Geological engineering Environmental science » Water science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country France Application Deadline 30 Sep 2025 - 00
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, IBDM employs around 220 permanent researchers, lecturers, engineers and technicians, as well as non-permanent staff on fixed-term contracts, post-docs, PhD students and trainees, spread across 21
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on individualised data; (2) to speed up FE model computation through machine learning prediction, in order to make it usable in clinical routine; (3) to conduct experimental validation of FE prediction results, in
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20 Feb 2025 Job Information Organisation/Company IMT Atlantique Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country France Application
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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