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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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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 15 days ago
programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Area of research: PHD Thesis Job description:PhD Position in Machine Learning for Single-Cell Genomics (f
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or analysis of facial expressions is an advantage. The ideal candidate must have excellent programming skills and experience with deep learning frameworks. Given the interdisciplinary character of AI:EMBODY Lab
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individuals and patients. These projects involve large-scale neuroimaging data collection at 3T and 7T, computational modeling of brain responses using machine learning methods, and cross-institutional clinical
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Autónoma de Madrid, and funded by the Community of Madrid. Among the tasks to perform are: Management and preprocessing of audio databases. Design, implementation, and testing of deep learning algorithms
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‑on experience with common machine learning / deep learning frameworks (eg. PyTorch or JAX) applied to biological or structural data. Solid Python programming skills, with experience building maintainable and
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, from linear models to deep learning, depending on what best fits a given problem. The most successful researchers will be driven by a curiosity for how their contributions fit into the larger picture of
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conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in-distributed-wireless-systems/ Distributed MIMO
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Neutral Infrastructure (dfCO2), this role contributes to Program 4: Machine Learning for Carbon Performance (https://dfco2.org.au/program_4 ) that aims to advance the next‑generation AI methods to model