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ambulance ECGs recorded since 2001, linked to the Danish national health registers and the Danish Heart registries, enabling robust validation of occluded coronary arteries and clinical outcomes. In addition
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Denmark. The work consists of quantitative research, including developing research questions, conducting theory-driven statistical analyses of longitudinal register data, and, where relevant, linking
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decision-making. A main challenge is finding ways to link AI-driven creativity with clear environmental performance feedback early in the design process. This phase is characterized by high uncertainty in
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mitigation and carbon-removal strategies in alignment with net-zero objectives. A key ambition is to extend assessment and modelling from individual buildings to urban districts and building stocks, linking
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research on architectures and methods for the real-time delivery of EO data from dense nanosatellite/CubeSat constellations and to develop innovative GNSS-based sensing methods and AI models to detect a
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/simulations). The role involves e.g. signal detection in noisy & interfering environments; signal filtering; signal compression; processing of measured data from mmWave RF signals; radar returns; quantum
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(such as heart disease, diabetes, and cancer) using, for example, data from registries and/or biobanks. The research will be performed in close collaboration with Center for Clinical Data Science (CLINDA
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project and process management, information management and data-driven approaches in the built environment. You will join a dynamic, interdisciplinary research environment and contribute to activities
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; measurement system characterization, e.g., validating platform performance such as dynamic range, linearity, phase noise and measurement uncertainty; automated test & data acquisition, e.g., using Python/MATLAB
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University (AAU) in Aalborg. This full-time position offers a unique opportunity to contribute to high-impact research in drones, AI, and human-computer interaction. Successful candidates will join a dynamic