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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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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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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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that combines machine learning and knowledge-based inference. In real-world applications, it is often paramount to exploit expert knowledge for the task at hand. However, this poses significant challenges with
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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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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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assets Practical experience in fuzzing or cybersecurity testing. Familiarity with machine learning concepts or AI platforms. Curiosity, creativity, and the drive to explore new research ideas. We offer
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for the position are : Obtained a first class Master in a relevant field, e.g. computer science, biomedical engineering or mathematical engineering Good understanding of statistics and machine/deep learning
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off-the-shelf sensors and the development of resilient algorithms that combine first-principles modeling with modern machine learning techniques. The goal is to push the boundaries of robust perception