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, where AI models are trained without having all data in a single computer. This makes it possible to use larger datasets for training, without sending sensitive data between hospitals. The goal is to
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tracking with simultaneous localization and mapping (SLAM). By leveraging semantic information, the aim is to improve data association, handle multiple hypotheses efficiently, and build consistent maps while
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the central node. The work includes developing scalable methods for information compression, robust detection of faulty or malicious nodes, and principles for handling uncertain or varying sensor quality
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model-based methods, the project enables systems that can detect, distinguish, and track multiple simultaneous events or objects using sensor information from, for example, radio, audio, and vibration
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uncertainties. Determining how to account for these uncertainties leads to research questions spanning from data collection and estimation to model representations and optimization algorithms. In the field
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robust, data‑efficient skill acquisition. Expert data offers fast, safe, and reliable initial learning, while reinforcement learning supports systematic exploration and adaptation to new conditions. By
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application! We are looking for a PhD student in Statistics with placement at the Division of Statistics and Machine Learning, Department of Computer and Information Science. Your work assignments As a PhD
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of 20 per cent of full-time. Your qualifications You have a master’s degree in electrical engineering, engineering physics, mechanical engineering, computer engineering, engineering mathematics or have
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different backgrounds. This position requires that you have graduated at Master’s level in in computer science, media technology, computer engineering, human-computer interaction, visual learning and
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per cent of full-time. Your qualifications You have graduated at Master’s level in electrical engineering, engineering physics, mechanical engineering, computer engineering, engineering mathematic