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details of 2-3 references to laura.cantini@pasteur.fr For more information : https://research.pasteur.fr/en/team/machine-learning-for-integrative-genomics/
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(iii) complex architectures with tightly coupled components hinder modular adaptation. To address these limitations, we research a physics-guided machine learning framework that integrates physical
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 22 days ago
, curious, autonomous, proactive and dynamic. A specialization in optimization, machine learning, statistical learning or game theory is appreciated. Research experience is a plus. LanguagesFRENCHLevelBasic
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, dimensionality reduction and/or machine learning methods (e.g., Lasso, ridge regression) is highly desirable. Familiarity with neurostimulation, Parkinson’s disease, or neuropsychological assessment tools is
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involving combinations of theoretical physics, mathematics, and machine learning. While machine experience is not a strict requirement for the job, we anticipate that the strongest candidates will have clear
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that preserves object identity or style. They should have a solid publication record in top-tier computer vision conferences such as CVPR, ICCV, or ECCV, and demonstrate proficiency in deep learning frameworks
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Polytechnique de Paris. The group conducts research at the intersection of statistical learning, machine learning, and data science, with a strong focus on structured data, representation learning, and
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: 10.1101/2025.09.08.674950), and AI/machine learning. We work closely with clinicians to translate our findings into clinical practice, focusing on genomically complex sarcomas and haematological
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between behavioral and computer scientists. The ideal candidate has some knowledge in both areas, and the specific behavioral domain is open to discussion. Project B – Understanding and Countering
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animals and humans, contacts with the environment are not avoided and sometimes even actively sought. We will deploy this inspiration from biology to design truly robust machines with distributed control