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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months 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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considered an advantage if you have: Experience with protein language models (e.g., ESM, ProtT5) Experience with structure prediction frameworks Experience in geometric deep learning or graph neural networks
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. demonstrated research capability in one or more of the following areas: quantitative modelling of dynamic or adaptive systems reinforcement learning, multi-agent systems, network or graph-based models simulation
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considered an advantage if you have: Experience with protein language models (e.g., ESM, ProtT5) Experience with structure prediction frameworks Experience in geometric deep learning or graph neural networks
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or adaptive systems reinforcement learning, multi-agent systems, network or graph-based models simulation of complex socio-technical or organisational systems causal inference, econometric analysis, or formal
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and Sobolev-type spaces (with Hytönen and/or Korte), Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic game
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equipment for biochemical, cellular and molecular biology experiments: centrifuges, spectrophotometer, electrophoresis, imaging equipment, etc. Excel spreadsheets, Word processing software, and graphing and
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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
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mitigate hallucination, misinformation, or bias in LLM-generated responses ・Leverage structured knowledge (e.g., graphs) and symbolic reasoning to enhance model robustness and interpretability ・Collaborate
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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