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from small everyday utensils, furniture and even larger building elements being dismantled and reused. These caring practices shape not only material flows but also people’s affective involvement
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applications Experience with at least some of the following methods is considered a significant advantage: Stem cell potency assays Cellular and molecular methods, including qPCR, immunohistochemistry, and
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machine learning methods for detecting, classifying, and identifying wireless anomalies in real-world radio environments. You will design and experiment with AI-driven approaches for spectrum analysis, work
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machine-learning based methods for analyzing IoT time-series data. Your Competencies: We seek a highly motivated researcher with a PhD in computer science, proficiency in software development, and a strong
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at least one of the following areas: theoretical computer science (e.g., formal methods, design automation, algorithms) and reinforcement learning. The candidate should be open to practical
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resources. The position is part of the AI RF Sensors research group, which develops AI-driven methods for RF, microwave, and wireless technologies in close collaboration with Danish and international
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the following areas: Air and surface sampling of fungi in buildings and on materials Basic statistical analysis knowledge (R or similar) and multivariate statistical methods Cross-disciplinary research
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methods, design interventions, and digital/computational methods to engage users, publics, and partners in exploring dilemmas, controversies, and paths to sustainable and desirable futures. We strive
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learning, for offshore industrial produced water treatment processes. The developed methods/solutions should be tested and demonstrated on a globally leading pilot-plant sited at Aalborg University
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computational methods to predict novel, disordered materials with application, e.g., for solid electrolytes in next-generation batteries. The position is part of the Villum Young Investigator project titled