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Field
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(or equivalent) in Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C
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Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C++); Proven interest in
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Interventions Our goal: To make digital health interventions more effective by predicting and improving adherence through Artificial Intelligence (AI) and machine learning (ML). Your colleagues
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in Austria. We are seeking a PhD candidate on predicting adherence to digital health-promoting interventions with Artificial Intelligence (AI) and machine learning (ML) techniques. You will develop AI
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subjects: sustainable aerospace, big data and artificial intelligence, bio-inspired engineering and smart instruments and systems. Working at the faculty means working together. With partners in other
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that has been generated in a prior laboratory-setting project. Specifically, we will integrate recent advances in artificial intelligence-based automated interpretation of medical images, and new knowledge
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range of science and engineering disciplines, from classical disciplines such as mathematics, astronomy and mechanical engineering, to interdisciplinary fields such as artificial intelligence,nanoscience
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and networks worldwide to become a truly global centre of knowledge. This 4-year PhD position is embedded in the Artificial Intelligence department at the Faculty of Science and Engineering and in
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Description We are looking for a PhD-candidate interested in topics that lie on the border of optimization by the use of heuristic algorithms and (Explainable) Artificial Intelligence ((X)AI). Specifically, in
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lengthy processing times associated with sequencing. This PhD project aims to develop innovative artificial intelligence (AI) methodologies by integrating histopathology images and RNA sequencing data