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faculty in developing theory and application tools for artificial intelligence (AI), and training efficient data analytics. 60% - Leading research in AI will include generative models, algorithms and
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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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, control theory, data science, data driven methods, discrete mathematics, graph algorithms, high-performance computing, integral equations and nonlocal models, linear and multilinear algebra, machine
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the WATOC Dirac Medal. As the successful candidate, you will contribute to the design and implementation of AI models that integrate quantum mechanical theory with deep learning, enabling rapid and
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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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structural and algorithmic graph theory. The purpose of the role is to contribute to the project “Algorithmic meta-classifications for graph containment”, working with Professor Matthew Johnson, Dr Barnaby
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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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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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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