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Field
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a doctorate. We are looking for: candidates with a Master’s degree in mathematics or a closely related field and with a strong background in probability theory. Prior knowledge in spatial stochastic
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experimentation to develop integrative metamodeling approaches, qualitative systems analysis methods and causal graph-based solutions for machine learning. The team aims to advance innovative and robust solutions
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a doctorate. We are looking for: candidates with a Master’s degree in mathematics or a closely related field and with a strong background in probability theory. Prior knowledge in spatial stochastic
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-based networks graph-based approaches Bayesian learning information theory Documented strong programming skills (preferably Python), for example with contributions to open-source projects, with an active
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contribute significantly to the preservation of the function and protection of the ocean for future generations. The Ocean Dynamics research unit at GEOMAR uses theory and numerical ocean models to advance our
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to model and analyse the intrinsic complexities of these systems. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains
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topics such as: neural networks self-supervised learning convolutional neural networks transformer-based networks graph-based approaches Bayesian learning information theory Documented strong programming
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full-time faculty members, whose principal research areas include Algebraic Semigroup Theory, Graph Theory, Mathematical Modeling in Medical Decision-Making, Mathematical Physics, Representation Theory
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quantum information theory, algebraic topology, polyhedral combinatorics, graph theory, and optimization are strongly encouraged to apply. For more details on the project, visit open positions page . To be
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transforming the theory into scalable production systems, backed by patented innovations. (3) A proven research record in mathematical AI (e.g., optimization, dynamical systems, graph theory, probabilistic