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://dimag.ibs.re.kr/ Successful candidates for the research fellowship positions will be new or recent PhDs with outstanding research potential in all fields of Discrete Mathematics with emphasis on Structural Graph
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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system to track proposals. Evaluate and perform preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results collected in accordance with the research
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, such as InfiniBand and Ultra Ethernet. Our project will also deliver a comprehensive set of PyTorch libraries, encompassing various optimized models for scientific applications, including Graph Neural
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Skills: Proficiency in Python, TensorFlow, PyTorch, or similar frameworks. MS or higher degree (PhD preferred) degree in Computational Neuroscience, Computer Science, Bioinformatics, or a related field
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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trials, and treatment guidelines, and transform these into structured knowledge graphs encoding relationships among histotypes, biomarkers, therapies, and outcomes. Assess the accuracy, completeness, and
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extreme weather to cybersecurity threats. Working within the LDTRC, you will undertake a range of research tasks, including: 1) Defining the ontology and knowledge graph architecture for a scalable digital
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apply machine learning and deep learning models (e.g., graph neural networks, generative models, transfer learning) for materials property prediction, interpretation, and inverse design. Perform high