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such risks, and on how to achieve a better use and exchange of existing protocols and data. The project includes the use of Digital Twins to simulate cascading disaster effects, as well as satellite and sensor
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Are you passionate about studying collaboration among organizations active in supply chain networks? Do you have a passion for applying cutting-edge quantitative methods and a desire to collaborate
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Organisation Job description Project and job description Our project will make use sensing technologies (hyperspectral cameras, NIR and Raman sensors), and an edge-compute AI pipeline to sort used
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interventions using AI? Join us to turn real-world sensor and app data into smarter, personalized digital solutions that support behavior change. PhD Candidate Predicting Adherence to Digital Health-Promoting
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Welcome to Maastricht University! Do you want to understand and predict how people engage with digital health interventions using AI? Join us to turn real-world sensor and app data into smarter
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, fabricating, and characterizing effective memristive devices, with the goal of forming device networks for realizing the physical learning paradigm developed at AMOLF. You will integrate these memristive
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seeking a highly motivated PhD student to join our team to work on the design and implementation of Oscillatory Neural Networks (ONNs) for physics-based computing applications. You as the successful
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of infrastructure networks, which remain fragmented and hinder the coordination needed to address cross-sectoral implications of demand changes (e.g., modal shifts, the use of home batteries). At the same time
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of diverse, deformable textiles at cycle times below one second, while hyperspectral, NIR, Raman, and RGB sensors feed an edge-compute AI pipeline for real-time decision making that routes each item
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There is an increasing demand for infrastructure services while, at the same time, infrastructure networks reach capacity thresholds. Extending and upgrading infrastructure is challenging and