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wireless communication, signal processing, digital, analog and mm-wave design, and machine learning. This is a unique opportunity to develop innovative, multi-disciplinary technology and shape future
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, machine learning) for energy applications, mostly focusing on reinforcement learning (RL), where you will consider innovative extensions (e.g., new neural network architectures) of state-of-the-art
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machine learning algorithms for anomaly detection, pattern recognition, and efficient data compression. To ensure practical usability, these models will also be optimized to run efficiently on edge hardware
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12 Nov 2025 Job Information Organisation/Company Vrije Universiteit Brussel (VUB) Research Field Computer science » Other Engineering » Computer engineering Engineering » Electrical engineering
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initiated research Advantages strengthening the candidate’s profile, but not explicitly required: Knowledge of machine learning and system optimisation; Python or MATLAB programming. Having published as (co
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power consumption trends or including the energy penalty of machine learning solutions themselves. And the energy efficiency at the transceiver hardware will be put in a broader perspective of
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resistance, via machine learning approaches. This doctoral project also foresees three secondments, each for the duration of three months, during which you will have the opportunity to visit partner
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. ASSIGNMENT Academic education You lecture various course units in the discipline of bioinformatics, data analysis of large-scale (bio)medical data, applications of artificial intelligence and machine learning
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communications systems with experts in wireless communication, signal processing, digital, analog and mm-wave design, and machine learning. This is a unique opportunity to develop innovative, multi-disciplinary
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, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing, graph theory, graph-signal processing, and