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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 world-leading fundamental and
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, you will work at the intersection of polymer processing, materials science and Machine Learning to develop dynamic recipes for sustainable plastics. In a typical plastics production line, several types
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is expected to generate novel analytical methodologies, peer-reviewed publications, conference presentations, and practical tools for forensic laboratories. During the PhD project, you will design and
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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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: fluctuating renewable energy, dynamic electricity prices, and increasing system complexity. In the future, industrial energy systems will not simply run. They will understand themselves. They will learn from
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representation learning. Programming skills (e.g., Python) and experience with deep learning frameworks (e.g. PyTorch) Interest in applications to ecological or biological networks Good analytical and
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strengthen your profile but is not a requirement for the position. The core academic profile sought is within process engineering or a related discipline. You should have an interest in and motivation to learn
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conducted as engaged scholarship, where research findings are developed in close dialogue with practice and contribute to ongoing strategic conversations, reflection, and learning. The successful candidate
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regard to methodological and theoretical approaches, and we welcome projects that incorporate artistic, practice-based, analytical, or interdisciplinary elements. The PhD student is, in close dialogue with
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implementation is an advantage Strong problem-solving and analytical abilities Ability to work independently and structure complex research tasks Interest in interdisciplinary research at the interface