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, metals and their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists of three full professors, one associate professor, 6 postdocs and about 15 PhD and 7
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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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of almost 11,000 individuals, including approximately 7,700 academic staff members, who passionately pursue answers to the profound questions that shape our future. Fueled by curiosity and a deep sense
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problems, statistical learning and machine learning (machine learning, deep learning) - Knowledge of associated software development tools and environments: Python, PyTorch, Scikit-learn, Jax, Julia
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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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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highlights, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation of teaching experience Academic Diplomas (MSc/PhD) You can learn more about the recruitment process here
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(optimal) solutions—with subsymbolic approaches such as deep learning and reinforcement learning to reduce the complexity of knowledge acquisition and search for solutions. Therefore, this project is closely
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comparing supervised and unsupervised methods (e.g., regularized regression, tree-based models, ensemble methods, clustering, dimensionality reduction) and deep learning approaches Developing and applying
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time