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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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27 Feb 2026 Job Information Organisation/Company Université du Littoral Côte d'Opale Research Field Mathematics Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage
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has a strong PhD program in Applied Mathematics, Applied Statistics, Graph Theory/Combinatorics, and Analysis, featuring active research collaborations both within the University and with external
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Aalto University, Dept. of Mathematics and Systems analysis Position ID: Aalto-PRA [#28168] Position Title: Position Type: Postdoctoral Position Location: Espoo, Southern Finland 02150, Finland [map
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with multitarget estimation for direction-of-arrival (DOA) detection and tracking in radar theory [12]. Graphs are a powerful data structure to represent relational data and are widely used to describe
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models. The project has the potential to bring together ideas from several different areas: mathematics (polynomials, probability, complex dynamics systems), theoretical computer science (computational
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and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) is a Bayesian information-theoretic principle in machine learning, statistics and data science. MML can be thought of in different ways - it
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within the theoretical and fundamental aspects of Mathematics, Physics, and Computer Science and Engineering, from various points of view and different abstraction levels. The selected candidate will also
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computer science, mathematics, statistics and/or probability theory. Support teaching at postgraduate level online. Moreover, you will: Have a good first degree or masters in a discipline relevant to computer
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Optimization: Mathematical Phylogenetics During this project, you will work on fundamental graph-theoretic and algorithmic problems in mathematical phylogenetics. Job description The Discrete Mathematics and