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renewable energy generation.KU Leuven leads Modelling and Optimization, which focuses on: Developing hybrid models combining first-principle and machine learning approaches. Creating predictive frameworks
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(PSI), within the research group EAVISE. The project explores audio representation learning for low-resource settings. Recent advances in machine learning for audio have focused on learning
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in international research visits if needed. We are looking for a highly motivated researcher with: A PhD in machine learning, computer vision, remote sensing, glaciology, climate science, or a related
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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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bipedal robot that will learn to walk on soft and natural ground, such as sand and gravel. The controller design will include knowledge of the type of ground the robot walks over, and how the substrate
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24 Sep 2025 Job Information Organisation/Company KU LEUVEN Research Field Computer science » Digital systems Computer science » Computer architecture Computer science » Programming Engineering
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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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organization-specific. This poses a critical challenge for applying machine learning: there are typically only a few examples of each specific fraud pattern to learn from. Classic machine learning methods
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engineering or mathematical engineering Good understanding of statistics and machine/deep learning algorithms Interest in Biomedical data science Excellent programming skills in Python Proficient English, both
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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