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Michael AlrøeView profile

Associate Professor

• Embedded Systems
• Robotics
• Internet of Things (IoT)
• +3 more

Michael Alrøe is an Associate Professor and Group Leader at the Department of Electrical and Computer Engineering, Aarhus University. With a strong industrial background spanning roles like Software Engineer, Project Manager, and Vice President, he bridges academia and industry. His expertise includes embedded systems, robotics, IoT, and digital electronics, with a focus on teaching bachelor and master courses. Primary Affiliation: Department of Electrical and Computer Engineering (Aarhus University) Academic Rank: Associate Professor Research Focus: Embedded programming, data communication, robotics His industrial experience includes projects for the Royal Danish Army, Navy, ESA, Airbus, and De Beers, such as electronic warfare monitoring systems, hull torsion measurement systems, and IoT-enabled fall detection systems. Recent research highlights his work on the ASE-BAN Testbed for Wireless Body Area Networks, emphasizing real-time experimentation and modular platforms.

• Artificial Intelligence
• Medical Robotics
• Embedded Systems
• +5 more

Jan-Matthias Braun is an Associate Professor at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark, specializing in Applied AI and Data Science. His research bridges artificial intelligence, robotics, and medical device engineering. Current projects focus on explainable AI integration in colon capsule endoscopy Development of real-time FPGA-based systems for colorectal diagnostics Biomechanical modeling for adaptive orthotic devices His work emphasizes cross-disciplinary applications of machine learning in healthcare, particularly for gastrointestinal disease detection and assistive robotics. Publications demonstrate expertise in deep neural networks, hardware acceleration, and smart environment control systems. Teaching responsibilities include: Advanced cybersecurity courses Deep learning applications in epilepsy detection Mentorship in capsule endoscopy image analysis