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quantification and robustness analysis in high-dimensional and noisy data. The models will be complemented with rigorous theoretical analysis and optimization methods, where tools from spectral graph theory
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supervised by Sebastian Throm. The subject area of the announced position covers kinetic theory, non-local diffusion and dynamics on graphs. The precise research direction will be determined together
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previous experience with e-graphs, or is familiar with theory and algorithms used by, for example, proof assistants, term rewriting systems, optimizing compilers, program analysis tools, constraint solvers
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. Proficiency in programming languages, compilation techniques and optimizations. Proficiency in C, C++, and/or Rust. Merits: Experience with e-graphs. Familiarity with theory and algorithms used by, for example
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covers kinetic theory, non-local diffusion and dynamics on graphs. The precise research direction will be determined together with the successful candidate upon personal background and interests
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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from