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(University College London, UCL). The workplace is Bergen, Norway About the work tasks: Investigate and explore ethical issues (e.g. human dignity, social justice, or human agency, human flourishing) in
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, but for example: University of Ulm (Germany): Algorithms for wearable data analysis University of Manchester (UK): Mathematical modeling of hormone rhythms Qualifications and personal qualities: We
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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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is part of the Faculty of Medicine and Health Sciences at NTNU. It was established as a Centre of Excellence in 2002, 2012, and again in 2023. Our Centre of Excellence, the Center for Algorithms in
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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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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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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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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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structure-preserving algorithms to generative modeling in AI. It will build upon the work done at IMF and SINTEF in this field. We will consider techniques like flow matching, and use ideas from optimal