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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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are looking for a highly motivated and skilled PhD researcher to work on structural surrogates of offshore wind foundations through graph-based machine learning. Our goal is to perform full-structure
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international workshops and conferences, presenting and discussing your research globally. Teach and contribute: Provide support for teaching activities and teaching innovation. Build up and apply skills: Build
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neuromorphic ultra-low-power active sensor readout and processing at the edge. The chip design will enable online learning capabilities, aiming at modulating the spatio-temporal filtering properties with
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all kinds of shops, on flights, in petrol stations, amusement parks...) and Ecocheques; Nursery near campus, discount on holiday camps; The space to form your job content and to continuously learn
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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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implementing signal processing algorithms specifically tailored to analyze signals that contain interfering impulsive content, often encountered in data coming from main and pitch bearings. Machine learning
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put
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increasingly complex networks. By deploying and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project tackles key challenges in anomaly detection