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. You are driven by scientific curiosity, enjoy working with complex multi-physics models, and are eager to advance probabilistic methods, machine learning tools, and simulation techniques. If you thrive
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, the MSCA Doctoral Network project SAILING (https://Secure AI and Digital Twin Empowered Smart Internet-of-Energy ) aims to establish a multidisciplinary network of leading research groups and industrial
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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employment conditions. More information about the department is available at: https://www.umu.se/en/department-of-computing-science/ The department's research on responsible and human-centred artificial
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. Probabilistic Digital Twin Synchronisation: Developing robust Bayesian frameworks and uncertainty quantification (UQ) to bridge the reality gap between real-world sensor data and high-dimensional computational
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-level technical skills derived from advanced doctoral training and research experience. The Associate Project Scientist will utilize expertise in probabilistic graphical models, causal inference
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a modern view of the statistical subject, where probabilistic models are combined with computational algorithms to solve challenging complex problems, as well as a statistical view of machine learning
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conditions. More information about the department is available at: https://www.umu.se/en/department-of-computing-science/ The department's research on responsible and human-centred artificial intelligence
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | about 2 months ago
applicant has a strong background in bioinformatics and/or probabilistic machine learning, as well as experience in omics data analysis, and possesses solid English-language skills. Experience with
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engineers and scientists, you will use these models for scalable vision tasks, instance segmentation, tracking, classification, and more. You will utilize probabilistic models to produce uncertainty-aware