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. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models for long-term forecasting. Collaborate closely with
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learning models such as Bayesian optimization, neural networks, random forests. A high proficiency in spoken and written English. Excellent communication and interpersonal skills. You are expected to learn
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during
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manufactured Mg alloys for biodegradable implant applications. Host: Helmholtz-Zentrum Hereon, Geesthacht, Germany, with PhD degree awarded by Kiel University (CAU), Germany. Read more about Hereon (https
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these changes affect ecosystem functions. To extend these analyses to new types of data and questions, we develop state-of-the-art hierarchical Bayesian methodology. We also actively apply our research to more
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, we develop state-of-the-art hierarchical Bayesian methodology. We also actively apply our research to more applied questions such as environmental management and risk assessment. For more information
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(https://www.hereon.de/index.php.en ) and Institute of Surface Science (https://www.hereon.de/institutes/surface_science/index.php.en ). Collaborators: Uppsala Universitet, Sweden and Quintus
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of the Biomaterials and Tissues of the Future. https://cordis.europa.eu/project/id/101226431 This network has 8 host institutions hiring doctoral candidates: Uppsala University, Universitat Politecnica de Catalunya
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foundations in classical probability theory and can be seen as a generalization of the Bayesian framework, bringing an additional degree of flexibility to express different types of uncertainty. In machine
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simulation of health-relevant biomolecular structure, dynamics and networks Computational modeling of health relevant signals that report biomolecular activity in model systems vivo Successful candidates will