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- Wydział Matematyki Fizyki i Informatyki UG
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-doctoral associate to work on one or more of the following topics: Mathematical Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be
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management and collection, causal inference, network analysis, graph theory, visualizations, and online tool development. Experience in conducting online controlled experiments is also desired, but not
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learning Broad familiarity with geospatial programming libraries Preferred Knowledge, Skills, and Abilities: Non-LLM foundation model expertise Time Series Foundation Models Expertise with Graph transformers
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ancestral recombination graphs to study of the genetic basis of diseases, incorporating ancient genomes • Apply and develop methods for partitioning heritability and estimating genetic correlations • Develop
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, Optimization, and AI • ML/AI for mobility prediction and optimization • Graph algorithms, network science • Spatiotemporal modeling • Operational research for mobility and infrastructure • Real-World Practice
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an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization algorithms Predicting structured output Self-supervised learning
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for the future of mobile and satellite communications. Fields of applications range from 5G/6G telecommunications to satellite-based internet connectivity. For details, you may refer to the following: https
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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times of Markov chains, random graphs and trees, random matrix theory, stochastic and Lévy processes in infinite-dimensional spaces, free probability, random sphere packings in high dimensions. About the
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Veterinary Medicine ● Variant discovery and genome annotation: Apply deep learning and graph-based models to improve variant calling, transcriptome annotation, and functional prediction in veterinary-relevant