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that reduce raw data at the sensor level. You will develop AI and machine learning algorithms for anomaly detection, pattern recognition, and efficient data compression. To ensure practical usability
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. Setting up, and testing optical spectroscopic measurement systems. Processing and analysing spectroscopic data using machine learning algorithms. Your primary workplace will be the VUB campus in Etterbeek
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systems and signal processing Responsibilities: Design of optical setup Optical system analysis and simulation Material characterization Developing AI/ML algorithms Literature review and preparation
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multimodal fusion, robust pose estimation, and real-time processing. Together, these roles form a cohesive effort to bridge algorithmic innovation with practical applications in next-generation media
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advanced algorithms that efficiently utilize UWB signal features (RSSI, channel impulse response, phase and amplitude data, Doppler maps,..) to support both high-precision localization and radar-based
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networks, etc. In this PhD position, you will build on our expertise in reinforcement learning for flexibility exploitation, and design of AI-based energy management algorithms to coordinate individual and
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fluid-structure interaction (FSI); - Development of techniques for deforming fluid domains, including Chimera techniques. The doctoral research needs to realize algorithmic improvements in the topics
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related field. The ideal candidate should have knowledge and/or experience in one or several of the following areas: Artificial Intelligence/Machine Learning algorithms and architectures Cybersecurity
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at the KU Leuven, within the Biomed lab (https://biomed-kuleuven.web.app/research ) to develop new trustworthy AI algorithms for fracture detection based on novel photon counting CT technology. Particularly
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with custom or commercial electronics, optics, mechanics, fluidics, and both embedded and application-specific software and algorithms. Our goal is to validate these systems in real-world environments