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develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 13 days ago
research visits, fostering the dissemination of the findings and collaborations within the academic community. The research topic focuses on fundamental developments of a novel learning framework for
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Applied Mathematics, Computer Science, or Theoretical Physics (at the time of appointment). Background in machine learning theory or in one or more of: high-dimensional probability, random matrix theory
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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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, statistics, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology
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of autonomous mobile machines integrating perception, reasoning, learning, action and reaction capabilities. The team's main research areas are: architectures for autonomous robots, human-robot interaction
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 1 day ago
, CIRAD AMAP, CIRAD PHIM, CIRAD PBVMT, INRAE ePhytia, INRAE IGEPP, INRAE LISAH, IRD EGCE, IRD IEES, Univ. Paris Saclay, TelaBotanica). This is a postdoctoral position in Machine Learning, more
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statistics and/or machine learning Specific knowledge • Proficiency in scientific computing • Knowledge of machine learning packages in Python or R • Proficiency in English (minimum level B2), as the postdoc
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perception for robotics; machine learning. o An interest for approaches based on foundation models. o Proficiency in open-source libraries: Pytorch or equivalent, OpenCV, Open3D, PCL, etc. o Programming
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results in leading conferences and journals Required Qualifications PhD in one of the following areas (or related fields): Machine learning / deep learning Quantum computing / quantum information Applied