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(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and
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European Molecular Biology Laboratory (EMBL) | Brandenburg an der Havel, Brandenburg | Germany | about 2 months ago
modelling, foundation models, cross-domain/-modality learning, explainable AI and mechanistic interpretability, representation learning, Bayesian inference, causal inference, active learning, AI-based agents
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at the beginning of employment. Position description The successful candidate will work within the research project “Advances in generalized Bayesian inference via differential-geometric methods” funded by
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the same systematics identified in the observables. d) Estimation of cosmological and “nuisance” parameters using Bayesian methods. 4. The research activities provided for the post-doc assignment will
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experimental and operational machinery datasets, including preprocessing, feature representation, uncertainty quantification and model validation. Investigating latent-variable, deep-learning and Bayesian
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-changing careers. Learn more about Sandia at: https://www.sandia.gov *These benefits vary by job classification. What Your Job Will Be Like: We are seeking motivated postdoctoral candidates to advance
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preprocessing, feature representation, uncertainty quantification and model validation. Investigating latent-variable, deep-learning and Bayesian approaches for learning informative health-state representations
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received funding from the European Innovation Council and the European Union’s Horizon Europe research and innovation programme under Grant Agreement No.101306664. Where to apply Website https
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partners; - analyze the behavioral traces and verbal data using suitable methods (regression, clustering analysis, Bayesian models); - contribute to the identification of relations between mediation
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, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph representation learning. Programming skills