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the theory of quantum graph states. Additional expertise in computational methods would be useful but is not necessary. The Postdoctoral and Senior Research Associate positions will also involve
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, and staff. Knowledge of student development theory and practice. Must be adaptable to change and able to work in a team environment while entrusted to complete individual projects, as needed. Must have
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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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graph theory. Qualifications Candidates with a Ph.D. in any area of cognitive neuroscience broadly defined (e.g., Psychology, Neuroscience, Computer Science, or a related field) are welcome to apply
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. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains. The position is hosted in the Leibniz Junior Research Group
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. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains. The position is hosted in the Leibniz Junior Research Group
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• Relational (deep) learning • Graph neural networks • Reinforcement learning These domains are central to our mission of advancing intelligent systems through principled, data-driven approaches
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because explainability is vital in health and medicine. Moreover, it leverages preferring simpler theories over complex ones if both give comparable levels of accuracy. Furthermore, it leverages the power
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Expertise in statistics, algorithms, graph-based software. Req Biostatistics skills-specific experience with statistical methods for data collection and analysis. Knowledge of healthcare quality principles
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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