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will employ advanced in vivo models and spatial technologies to dissect the roles of tissue-resident macrophages and infiltrating immune cells along the periphery–immune–brain axis. About us The research
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partially filled containers – is a major challenge in aerospace, transport, and energy systems, where it can compromise stability and safety. The PhD will focus on developing low-order models of sloshing
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improve the efficiency, maneuverability, and noise performance of drones and other multirotor aircraft, but their deployment requires more advanced modeling and control methods. The PhD will focus
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-pathogen interactions using in vitro model systems mimicking chronic diseases. The project foresees ample collaborative opportunities with research groups in the MICRO-PATH consortium, spanning
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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
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1-D and 2-D modelling approaches fail to capture the complex three-dimensional effects critical to high-frequency, high-efficiency operation in modern converters. The PhD candidate will: 1
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Artificial Intelligence to improve on the current state-of-the-art in articifial models of social cognition and emotion recognition. In Neurosymbolic AI, logic and symbolic AI methods are used to improve
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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analyse levels of identified proteins in a larger patient population as well as an animal model of the Fontan circulation.• You will use in vitro techniques and immunohistochemsitry on an animal model