326 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Simons-Foundation" positions in Denmark
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in electrical engineering, computer engineering, computer science, or similar. Strong background in communication systems, optimization, or machine learning for networked systems. Experience and
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in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a closely related field Has strong theoretical and practical experience in deep learning Has hands
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imaging, deep proteomics, metabolomics, metaproteomics, and machine learning (ML) approaches to develop diagnostic classifiers, spatial tissue atlases, and identify potential therapeutic targets
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sustainable machine learning approaches and addressing renewable energy related projects. Likewise, deploying a novel paradigm of KGML (knowledge guided machine learning) can propel further research. PhD
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initiatives Commitment and ability to teach and supervise students at bachelor’s and master’s levels, including course development in digital design, computer architecture, and AI hardware Strong communication
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PhD from the University of Nantes in France. He has worked 10 years at the university of Aalborg focusing on the development of statistical methodology for application in machine learning and
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of machine learning Distributed and federated training The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics
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the Machine Learning and Artificial Intelligence. Solid mathematical and analytical skills. Knowledge about statistical machine learning, robotic perception, multimodal AI algorithms. Experience in programming
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systems with digital technologies from a socio-technical perspective. This includes human–machine interaction, XR-based interfaces, and engineering solutions for hybrid production systems. Candidates should
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processing, and machine learning techniques is considered an advantage. We are looking for a motivated, proactive, and curious PhD candidate that enjoys working across disciplines and contributing to a shared