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is driven by the increasing introduction of renewable energy sources (RES), both in large-scale installations (wind farms, solar) as well as distributed over large number of small-scale assets (e.g
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-intensive, often being non-interpretable, and being highly brittle towards shifts in data distribution and changes in the problem domain. This fully-funded three-year project, with the possible further
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-scaled CMOS models. Advanced algorithms and architectures need investigation of power-consumption trade-offs. For instance, digital predistortion (DPD) can enhance power amplifier efficiency but
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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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further developing algorithms and writing an application to develop a wearable device functionality for monitoring patient behavior monitoring based on the gained insights. We value candidates who: have
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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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manner, and (2) design AI approaches such as learning algorithms and reasoning engines that can exploit the provided knowledge? The successful candidate will conduct research on how to build software
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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