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Knowledge of deep learning architectures, graph neural networks, or uncertainty quantification Familiarity with HPC environments Language Requirements: Applicants must demonstrate at least B2-level
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 2 months ago
the Fugger lab at the Oxford Centre of Neuroinflammation , focusing on the development of drugs that tame common brain diseases through the application of graph-based neural networks, deep learning, and
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(Position P): Agricultural AI data integration and management based on LLM In the context of the MSCA JD project GreenFieldData https://www.eu4greenfielddata.eu/ GreenFieldData: IoRT Data Management and
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; Socioeconomic and demographic data; Hospital admissions and mortality records; Develop physics-guided and graph-based models for high-resolution environmental exposure estimation; Build explainable AI pipelines
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Tomon. More information about Discrete Mathematics at Umeå University can be found on the group’s homepage: https://www.umu.se/en/research/groups/discrete-mathematics/ As a PhD student, you will be part
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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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admissions and mortality records; Develop physics-guided and graph-based models for high-resolution environmental exposure estimation; Build explainable AI pipelines to identify which exposures matter most
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maps. Knowledge graphs can be used to model these transformations and to link geodata sources to questions. In this project we will apply symbolic and sub-symbolic AI methods to scale this up across
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tools without disrupting established practices? iii) Technology Assessment: To what extent do representational approaches such as ontologies or knowledge graphs meet the identified requirements, and where
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-driven forward and inverse design Experience in the construction generative artificial intelligence on material design Experience in the application of graph neural networks on inverse design Experience