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at University of Copenhagen. The Doctoral Candidate will be supervised by Professor Petra Daryai-Hansen. IRP13 is embedded into Work Package 4 (WP4) ‘Language learning and plurilingualism in the era of GenAI’. In
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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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. Learn more about the department on our website and in our research portal . Your work tasks This PhD stipend will be affiliated with a research and innovation project focusing on health system innovation
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has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct
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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
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links to individual PhD stipend calls. New research areas may be added until the application deadline. Beyond the research conducted during the PhD project, a successful candidate is expected to teach
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PhD Scholarship in Development of Cement-Free Living Building Materials for Sustainable Construction
commitment to research excellence Possess the ability to work independently, take initiative, and drive research activities forward Show adaptability and a willingness to learn new concepts, techniques, and
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• Participate in the department’s research environment • Complete a PhD training programme • Teach at one or more of the department programmes Your main task as a PhD student will be to develop and complete a PhD
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Other relevant professional activities Curious mind-set with a strong interest in enzyme structure and function An ability to learn new skills An ability to interact professionally with other scientists
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implement a hyperspectral imaging system tailored to bulk forensic trace analysis and develop chemometric and machine-learning models for material identification and classification. You will evaluate