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interdisciplinary pipelines to identify effective RNA drugs against human diseases: AI-driven RNA discovery – using AI agents to prioritize the therapeutic potential of human lncRNAs (supported by the NHLBI
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translational questions, including but not limited to drug efficacy and safety prediction, mechanism-of-action inference, biomarker discovery, causal or network-based modeling of biological systems, and drug
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research projects in computer vision, machine learning, AI, and robotics. Projects may include physically-grounded AI guidance agents, modeling of multimodal data, and generative AI systems for situated
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World Asset Develop new agentic AI and large language models (LLMs) to support the algorithm designs for Real World Asset applications and ecosystems. Implement and test the Real World Asset framework
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study its impact on the degree of collaboration in hybrid teams. The successful candidate will: Develop algorithms to model team performance based on interpersonal (e.g., monitoring, communication) and
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including multiple and embodied AI agents. Countering the current trends of very large models with hard-to-control outputs, we will focus on balancing data-based approaches with artists’ knowledge and search
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but not limited to drug efficacy and safety prediction, mechanism-of-action inference, biomarker discovery, causal or network-based modeling of biological systems, and drug repurposing or design
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for high resolution imaging, developing computational models of islet blood flow changes; characterizing and applying novel ultrasound contrast agents including molecular targeted agents; and use of contrast
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processes and their associated mass transfer phenomena without and with chemical reaction. Demonstrated understanding of and/or practical or modelling experience with liquid absorbent-based CO2-capture
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Activity of CD8(+) T Cells. J Am Chem Soc.145(23) (2023) 12701-16. Research focus We aim to map T-cell interaction landscapes in complex immune microenvironments, understand how therapeutic agents act from