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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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microscopy and SEM, with computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties
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modeling, computer simulation, non-linear model analysis, interactive learning environments and decision-laboratory experiments. About the project/work tasks: Description of the INTEGRATOR project
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population genetics and marine metapopulation dynamics. Contribute to generating and managing reproducible computer code and workflows. Produce a high-quality PhD thesis, comprising of manuscripts suitable
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TOEFL or IELTS. To be considered for admission, applicants must have a minimum TOEFL score of 575 (written test)/233 (computer-based test)/90 (internet-based test) or a IELTS score of 6.5. In some cases
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-Computer Interaction (HCI) perspectives to investigate how AI companionship technologies may reshape experiences of care, companionship, emotional support, wellbeing, and autonomy among older adults. Your
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. The center investigates emerging forms of digital narrative and how cultural contexts are reshaping their use and function. The PhD fellow will work in the research group Computer Games and Interactive Digital
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or human-computer interactions studies. The focus of the PhD-thesis needs to contain knowledge areas such as learning theory, cognitive theories with applications on studies of learning, design of learning
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learning activities in courses related to communications and networking Required selection criteria You must have a relevant master’s degree in wireless communications and mobile networks, computer networks
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or human-computer interactions studies. The focus of the PhD-thesis needs to contain knowledge areas such as learning theory, cognitive theories with applications on studies of learning, design of learning