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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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platform. Initially, a black box deep learning approach will be implemented. However, due to the need for robustness, transparency, and explainability (e.g. for quality control across sectors), the research
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unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many
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for anomaly Detection and diagnostics: Leveraging state-of-the-art machine learning and deep learning models for automated fault detection, classification, and time-till-failure prediction. This will involve
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at Ghent University from September 2025. The project aims to develop a minimally to non-invasive treatment for focal epilepsy with ultrasound neurorecording, modulation, and deep reinforcement learning
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programming. You are highly motivated to conduct (applied) research at the intersection of (deep) machine learning and the health sciences. You have good programming skills in languages such as Pythorch, and