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- Wydział Matematyki Fizyki i Informatyki UG
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/ 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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classes and their roles in scientific applications, such as deep neural networks (DNNs), convolutional neural networks (CNNs), transformer models, and graph-based neural networks. Familiarity with software
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scientific writing skills. Design and performance of experiments, creating graphs, knowledge of statistics, interpretation and dissemination of data. 3-years of mentorship of junior technicians and trainees
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and machine learning. Topics of interest in this area include, but are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge
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graphs and related structures, limit theorems, stochastic calculus and applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department
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of groups that are of interest in the context of rigidity, group cohomology, noncommutative geometry and index theory, harmonic analysis on groups, expander graphs and high-dimensional expanders. Required
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
No. 2083, id.2294) and a research team at the University of Glasgow led by Leroy Cronin that utilizes graph theory to explore molecular complexity (Philosophical Transactions of the Royal Society A
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. Demonstrated experience with electrophysiology data analysis (human or animal). Experience in graphing, statistical analysis and data management skills. Certifications/Licenses Required Knowledge, Skills, and
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on either uncertainty quantification or how uncertainty should be expressed to users. https://unit.aist.go.jp/deihrd07/keiyaku_koubo/2025-airc_0043.html [Work content and job description] ・Develop uncertainty
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conclude on December 31st 2029. The goal of this research effort is to apply machine learning (ML) techniques, in particular (equivariant) graph neural networks to accelerate the creation of all physical