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to the success of the whole institution. The Faculty of Computer Science, Institute of Theoretical Computer Science, thenewly established Chair of Algorithmic and Structural Graph Theory offers a position as
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of extensive-form games. These studies highlighted the effectiveness of combining tools from graph theory, mathematical programming, and dynamic programming to compute pure Nash equilibria in extensive-form
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the department is available at: https://www.umu.se/en/department-of-computing-science/ Project description Graph transformation is a well-established theory that studies computational methods
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spectral graph theory. The PhD will be supervised by Anurag Bishnoi.You will have the opportunity to collaborate with Postdocs, PhD candidates, and other faculty members of the research group. You will also
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transformation over the time. Taking this research further may mean considering the dynamics of the precedence graph, with a more general mathematical formulation and dedicated exact or heuristic methods. It may
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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counterfactual reasoning frameworks that uncover latent mechanisms and enable principled hypothesis testing. Our goal is to advance the theory of representation learning and causal inference in high-dimensional
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Do you like applying mathematical theories in practice to solve real-world challenges? Do you like working with top-notch, internationally recognized industrial partners? Would you like to push the
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 2 months ago
possess one or more of the following: Bachelor?s degree in Computer Science, Statistics, Mathematics, or Economics (with a strong quantitative orientation). Master?s Degree preferred but not required
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addition to CSK from existing KBs) in targeted avenues such as multipurpose robots, along with mathematical modeling and algorithmic insights Counterbalancing issues such as bias, overfitting, and inexplicable