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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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to imagine novel task configurations and learn robust manipulation policies from just a few real demonstrations. You will work at the intersection of 3D computer vision, physical simulation, and robot learning
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extensive knowledge on zooplankton imaging techniques ability to program and train machine learning models for automated image classification experience with shipborne campaigns and ready to join multi-week
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Machine Learning, Computer Science, Mathematics, Statistics, Physics or a closely related field and want to join the mission of unlocking the “geometry of artificial intelligence” then come join us! Join us
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to develop generative AI methods for nanoparticle drug delivery design, at the intersection of machine learning, explainability, and pharmaceutical nanotechnology. Job description We are looking for a
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prestigious ERC Consolidator Grant in a great interdisciplinary institute in Amsterdam. Join us! Are you passionate about machine learning, natural language processing and generative AI? We are seeking a
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, the fusion community has started to develop fast surrogate models based on Machine Learning / AI models to speed up significantly the employed tools. Such tools have demonstrated to be generally applicable and
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models that provide evidence-based reasoning for mission-critical decisions. Explainable AI for mission-critical decision support: design interpretable machine learning architectures capable of offering
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/postdoc-ub4h-digital-technologi… Requirements Specific Requirements A PhD in educational sciences, engineering education, learning sciences, or a related discipline. Knowledge of hydrogen technology or
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and digitizing archival data, strong knowledge of causal inference methods, good command of R and Python. Knowledge of machine learning methods is an asset. Strong command of English; command of either