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of computational algorithms for the analysis of complex and high-dimensional data. Required teaching duties: The appointee’s teaching duties will be defined as part of the teaching plan of the Department
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at the intersection of algorithmic guidance and management. These issues have become more pressing as generative AI offers potential productivity gains while also making it easier to conceal effort. A first study
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data
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. Inference methods will be developed within a maximum likelihood framework, based on Newton–Raphson-type optimization algorithms. Particular attention will be paid to the structural constraints imposed by sum
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data
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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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-of-the art in animal breeding, human genomics, ecology and evolutionary biology. The post-doc will thus work with a cross-disciplinary team of researchers and can contribute towards the development of methods
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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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interpretable framework for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured
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"Gheorghe Mihoc - Caius Iacob" Institute of Statistical Mathematics and Applied Mathematics of Romanian Academy | Romania | 2 months ago
machine learning algorithms, using specific software libraries (PyTorch, TensorFlow, scikit-learn or equivalent), processing and visualizing biomedical data, and using software versioning and collaboration