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, clustering analyses, propagating location and other uncertainties...) of mid-ocean ridge catalogs, using standard, Bayesian and machine learning techniques. ⁃ Implement methodologies that improve estimates
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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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strategies to mitigate impacts on adjacent waters. Research activities include: coordinating with multiple stakeholders and collaborators to define objectives and research questions; leading participatory co
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In this role, you will be responsible of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with
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, rental platforms, and production systems—where decision-making must balance conflicting objectives, leverage real-time data, and ultimately support sustainable profitability. Examples include optimizing
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uncertainty. Relevant areas include interpretable probabilistic, causal, Bayesian, and knowledge-based networks. While expertise in electromagnetics is not essential, applicants must be willing to broaden
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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute
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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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) Experience in the use of neuroimaging analysis (fMRI, MRI) to study mechanisms of brain function Previous experience of using Bayesian methods in both model development and fitting. Previous experience and
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 2 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine