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Field
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, where AI models are trained without having all data in a single computer. This makes it possible to use larger datasets for training, without sending sensitive data between hospitals. The goal is to
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machine learning approaches to link gene-regulatory programs to neuronal phenotypes. Use explainable sequence-to-function models to interpret regulatory logic underlying neuronal identity and function
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outcomes Synthetic data generation (virtual patients) Statistical model checking to ensure statistical correctness of the results Machine learning–based classification and regression methods The candidate
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data. Develop and apply machine learning models to estimate uncertainty in climate impact statements. Analyse spatial and temporal patterns and trends in climate-extreme impacts. Cross-validate
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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therapeutic strategies, ranging from antibacterial to anti-cancer drugs. To this end, we develop new ways to combine state-of-the-art technologies in metabolomics with mathematical modeling. The candidate will
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have strong programming skills in Python; You have knowledge of medical image processing, and machine learning and deep learning techniques; Written and spoken proficiency in (scientific) English is
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About us VIB.AI, the VIB Center for AI & Computational Biology, is a research center dedicated to integrating machine learning with deep biological insight to understand complex biological systems
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Intelligence (AI) and Machine Learning (ML) capabilities into distributed cloud-edge infrastructures to enable autonomous and efficient network/service management. The PhD candidate will investigate innovative
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Job related to staff position within a Research Infrastructure? No Offer Description PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1