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, unlocking reliable perception and navigation where GNSS/GPS cannot be trusted or is unavailable. The project combines ultrasonic sensing, probabilistic perception, and machine learning with advanced robotics
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following thematic areas: • AREA 1: Machine learning and AI-driven methods for design, simulation, and optimisation in architectural and construction engineering. • AREA 2: Robotic and additive
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communication, applied AI, or a related field. You have experience with machine learning and signal processing, and preferably knowledge of RF or wireless systems. You can work with experimental data (e.g
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requirements, including link budgets, beam steering, and orbital pointing dynamics. • Experience with optimization methods and physics-informed machine learning. • A strong publication record in antennas
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substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also
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biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be
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written and spoken Willingness to engage in interdisciplinary collaboration and fieldwork Advantageous: Knowledge of bat ecology and species identification Experience with machine learning or automated
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substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also
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students in its 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
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Are you interested in real-time distributed systems, IoT connectivity, and AI-driven automation? The Department of Electrical and Computer Engineering at Aarhus University invites applications for a