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modeling, satellite remote sensing, or machine learning Programming skills (Python or R) Strong publication record
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at the interface of biostatistics, machine learning, and biomedical data science. This mentored postdoctoral position is designed to support the development of an independent research trajectory in methodological
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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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science. Familiarity with ship data processing, ship performance analysis, machine learning algorithms (Deep learning, Reinforcement learning, etc.). Proficiency in written and spoken English - essential for data
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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/DPhil in robotics, computer science, machine learning, informatics, AI, or a closely related field. You will have an excellent academic track record in topics relevant to locomotion and manipulation; path
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programming, large-scale optimization, and simulation-based optimization; and/or Expertise 2: Experience with general methods in artificial intelligence and machine learning, including reinforcement learning
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machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks to minutes thus
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Learning Sciences, Computer Science, Human-Computer Interaction, Informatics, Educational Measurement and Statistics, Educational Psychology, or a related field. Required Qualifications: Relevant experience
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 19 hours ago
) Optimization and parameter identification methods Data-driven modeling and machine learning Physics-Informed Learning (or hybrid modeling approaches) Handling and analysis of large-scale datasets (e.g., mobility