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, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted
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with statistical and numerical analysis methods as applicable to strain design problems is a distinct advantage. Familiarity with machine learning tools such as PyTorch, HuggingFace transformers and
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working with 3D Image analysis in collaboration with users from many different fields. A central part of the team’s activities lies in creating new tools using machine learning enabling better and faster
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for designing, discovering or characterizing advanced materials, preferably combined with a strong expertise in AI-accelerated materials discovery, e.g., machine learning, graph-based models, generative AI, data
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transition - researching wind farms, hybrid power plants, and emerging technologies. Its 25 members employ multi-disciplinary design optimization, systems engineering, uncertainty quantification, machine
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candidates in any field of machine learning theory, algorithms and computation or in AI applications in e.g., the social, neuro, or natural sciences, are encouraged to apply. The position comes with a start
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chance to start a research group within thermodynamics or machine elements – as well as to support our existing activities within materials research. Our section is diverse in nationalities, personality
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on critical applications (both human and machine type). General knowledge of the physical layer is expected, but the main research will focus on the protocol layers above. General knowledge is also
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learning and their application for the life sciences industries. The research aims to advance computer-aided methods for the design, scale-up and optimization of processes and systems for life sciences, bio