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Field
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 9 days 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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: Operator Algebras, Machine Learning, Analytic Number Theory, Automorphic Forms and Representation Theory Appl Deadline: 2025/10/10 11:59PM (posted 2025/09/10, listed until 2025/10/10) Position Description
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involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and
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to conduct multidisciplinary research around robot learning for autonomous robotic chemists, with a background of excellent research outputs across Robotics and Machine Learning, ideally with a background in
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of research and education, covering all aspects of computer science, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering, networking
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semantic representation models for sign language. Such representations are key to allowing SL to be efficiently processed by large language models (LLMs), and will lead to machine learning models that can
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fields. Strong programming skills in Python, Java, C++, etc. A solid foundation in generative AI, machine learning, and related areas. Interest in Speech/Language Processing and its application. Know-how
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to conduct multidisciplinary research around robot learning for autonomous robotic chemists, with a background of excellent research outputs across Robotics and Machine Learning, ideally with a background in
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employ cutting-edge single-cell and spatial omics technologies with bioinformatics and machine learning to decipher principles of gene regulation underlying cell identity and its disruption in human