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develop a new generation of hybrid models combining large-scale machine learning with physical knowledge to represent interactions between mobile robots and their environment. The research will address
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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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computational models and machine learning methods, as well as experience in repertoire data analysis. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8023-CLAMAR-001/Default.aspx Work
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consensus. The successful candidate will develop the algorithmic foundations of this framework—making the motion-generation stack modular and heterogeneous (combining model-based control with learning-based
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on lncRNA structure. These experimental constraints will then be used to guide deep learning-assisted RNA 3D structure prediction tools, in order to generate ensembles of structural models. Clustering and
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learning, particularly flow-matching generative models and protein language models. The research will focus on designing efficient generative models able to produce realistic conformational ensembles while
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for projections. This project aims to explore the coupling of the ocean and ice-sheet model components via a machine learning emulator of ice-shelf cavity circulation. While the ultimate goal of the project is to
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, ideally molecular dynamics and/or DFT. Scientific programming skills, particularly in Python, are expected. Familiarity with machine learning or generative AI methods applied to materials would be a strong
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, machine learning, explainable artificial intelligence (XAI), digital twins, and integrated data-model approaches. • Study of the frugality of the developed approaches by reducing the requirements
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data) will help validate observations and refine predictive models. Automated monitoring tools (scripts, dashboards, alerts) incorporating machine learning algorithms or statistical methods will be