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analysing algorithms and metrics for ensuring fair treatment of groups defined by protected attributes (e.g. gender, age, ethnicity, …). Theoretical relations between fairness notions: studying
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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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vendors remain difficult to identify and assess. These dependencies arise not only from technical limitations but also from interactions among algorithms, data flows, platform architectures, workflows, and
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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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characterized by the evolutionary loss of MHC class II molecules and CD4 T cells — components that are essential for immunocompetence in other vertebrates, including humans. We combine classical molecular biology
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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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databases, information retrieval, data mining, or algorithms is expected. Excellent written and oral English language skills are required. The appointment is to be made in accordance with NTNUs guidelines
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companies. The research will integrate techniques of numerical analysis and structure-preserving algorithms to generative modeling in AI. It will build upon the work done at IMF and SINTEF in this field. We
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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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analysing algorithms and metrics for ensuring fair treatment of groups defined by protected attributes (e.g. gender, age, ethnicity, …). Theoretical relations between fairness notions: studying