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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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interpretable framework for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured
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16 Jul 2026 Job Information Organisation/Company CNRS Department Laboratoire lorrain de recherche en informatique et ses applications Research Field Computer science Mathematics » Algorithms
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, contributing to research in the field of artificial learning. The data generated by this doctoral work will be deposited on open archaeological databases (Nakala, Huma-Num, POP, etc.), and the algorithmic code