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swimming. The successful candidate will lead quantitative analysis of animal configuration and related flow field topology utilizing graph theory to assess the impact of swimming and environmental conditions
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to characterize the brain network interactions using connectivity and graph theory metrics. Develop reproducible computational pipelines in MATLAB, Python, R, or similar platforms for electrophysiology processing
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the dissertation, must be completed by the date of appointment. Demonstrated research experience with density functional theory or closely related first-principles atomistic simulation methods. Demonstrated research
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fundamental research in physics-informed and symmetry-aware machine learning for nonadiabatic excited-state molecular dynamics. Develop and evaluate equivariant graph neural networks and related architectures
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Carolina is inviting applications of two postdoctoral associates in the area of mathematical foundations of data science, AI, Graph Theory, Optimization, Probability Theory in a broader sense. The candidates
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-learning models (e.g., graph neural networks, equivariant architectures) in collaboration with computer science researchers. Applying developed models to problems in Earth and planetary interiors, such as
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in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date machine learning
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systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics and mathematical approaches to signal analysis
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with Prof. Mauro Maggioni on problems that include: analysis and algorithms on graphs, geometric analysis of high dimensional data sets, dynamic data sets, scientific machine learning, high-dimensional
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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of