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, Computational Neuroscience, Computational Psychology or Behavioural Science; Transport Modelling, Transportation Science or Urban Mobility; Data Science, Artificial Intelligence, Machine Learning
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organizational theory, the learning sciences, digital transformation, digital technologies, human-computer interaction, and related fields. Within the specific field, the PhD student will engage in both research
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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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) in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related quantitative field. Further qualifications: Technical Skills: Advanced proficiency in Python and deep
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advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
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Engineering, Machine Learning, Applied Mathematics, or a related field. A strong academic background and interest in AI systems, embedded intelligence, edge computing, machine learning, or related areas. Strong
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practices, and technology-enhanced learning. It is an advantage if you have one or more of the below A solid foundation in understanding learning processes from a cognitive, embodied, and/or epistemic
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industrial energy systems that combine physics and data to become adaptive, autonomous and trustworthy? To get there, you will work at the intersection of thermal energy systems, machine learning and
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-Physical Energy Systems The PhD position focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be
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focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be designed and deployed efficiently