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description In the Medical Informatics and Image Analysis group, we seek a highly ambitious and internationally recognized early-career researcher with a strong track record of scientific excellence to advance
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the interplay between qualitative and quantitative methods and data. There is a growing focus on novel computational methods such as NLP, machine learning, and AI within the group. Teaching activities in
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closely with other members of the AI:EcoNet Lab. Requirements: Master’s degree in computer science, data science, mathematics, statistics, physics, software or relevant fields Strong background in machine
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, distributed algorithms, secure multiparty computation, and information theory. You are expected to speak and write in English at an academic level, and preferable also in Danish. Qualification requirements
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programme management as well as contributions to the development of teaching activities and teaching material. Emphasis is placed on the extent, to which the applicant, has developed the academic discipline
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, to various qualitative methods; also emphasizing the interplay between qualitative and quantitative methods and data. There is a growing focus on novel computational methods such as NLP, machine learning, and
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, course/study programme management as well as contributions to the development of teaching activities and teaching material. Emphasis is placed on the extent, to which the applicant, has developed
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leading workshop and laboratory facilities, where research and innovation are carried out in direct collaboration with industry to a great extent. The position is offered in relation to the research program
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teaching at different levels. Including tests/exams, course/study programme management as well as contributions to the development of teaching activities and teaching material. Emphasis is placed
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interdisciplinary competences in: communication theory, networking, information theory, physics, mathematics, computer science, and statistics. This PhD project falls under Research Thrust RT2 on Physics-based