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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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, 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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of the project is to use machine-learning assisted molecular dynamics simulations incorporating quantum effects for the identification of new variant-specific drug targets which will be validated experimentally
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 3 months ago
the IDPFold project (2025-2029) recently funded by the French National Research Agency (ANR). The main goal is to develop geometric deep learning models to study intrinsically disordered proteins (IDP). The PhD
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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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Background in computational neuroscience Knowledge of machine learning Basic understanding of neuroscience Experience with numerical simulations Where to apply E-mail srdjan.ostojic@ens.psl.eu Requirements
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learning new techniques and finding answers to problems. You want to take advantage of the opportunity to do your PhD in two different countries and learn from different cultures and expertise. Where
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climatic conditions, using machine learning approaches based on isotopic data. SSIAs for δ13C, δ15N and δ34S in dentin collagen and δ66Zn in enamel to reconstruct the evolution of seasonal habitats and the
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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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the field of algorithm configuration and selection in a streaming fashion by investigating techniques that continuously optimize machine learning models as new data instances arrive [2]. A key focus will be