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Field
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aerosols. - Visualization and post-processing tools: Proficiency in visualization tools (e.g., Python) and data post-processing tools to analyze simulation results and generate graphs, maps, and diagnostics
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fundamental research in physics-informed and symmetry-aware machine learning for nonadiabatic excited-state molecular dynamics. Develop and evaluate equivariant graph neural networks and related architectures
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parameters from parasite genetic data; cf., viral genomic epidemiology, where bifurcating trees capture the ancestry of DNA sequences, and human population genetics, where the ancestral recombination graph
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the images and extracted text, to generate and align with existing collection metadata Align entities with existing entities/records, including the objects themselves to bootstrap knowledge graph creation
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. It is an advantage (though not mandatory) if you have prior knowledge in ontologies, knowledge graphs, and/or robotics. You have demonstrated your excellent skills by outstanding grades during your
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researchers in algebra, combinatorics and related disciplines, including finite geometry, algebraic graph theory, permutation group theory, coding theory and extremal combinatorics. The Centre offers a dynamic
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fields: Agentic AI, Large Language Models, Artificial Intelligence, Biomedical Ontologies, Biomedical Knowledge Graphs, Computational Biology, Bioinformatics, Biomedical Informatics or a related field
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Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
/ Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date machine learning libraries Excellent written and verbal communication skills Track record of publishing
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addressing research questions relevant to data science, biology, and agroecology the aim is to improve data flows and create knowledge graphs and future visions of landscapes. The tasks will encompass