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- Control of Communication networks - Markovian Processes - Network Based Localisation / Radio based connectivity - Adaptive bandwidth - Mesh networking - Wireless Sensor Networks - Edge Computing - Time
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-based Exploration - Source localization b. Perception in sensor-degraded environments: - Localization in smoke and dust filled environments - Scene awareness - Biometric/triage evaluations, etc. c
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to have good knowledge of computer science, mathematics, algorithms, and programming. Knowledge and experience in artificial intelligence and machine learning is expected, but not required. Knowledge and
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, algorithms, and programming. Knowledge and experience in artificial intelligence and machine learning is expected, but not required. Knowledge and experience in deep learning and generative AI is considered
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; they make sense to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project
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theoretical research, algorithm design, and the development of software tools that demonstrate the applicability of the new methods. Research environment The positions are hosted by the Department
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to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project includes both
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on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems
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integrated sensor arrays. The project combines several different concepts: Progress in understanding insect neurobiology that provides proven circuit designs to solve significant problems such as autonomous
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from manufacturing sensors and XR headsets (video, audio, motion). This includes building multimodal AI pipelines, generating procedural representations, and contributing to the integration of real-time