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understanding of placental development through the integration of computational modeling and clinical imaging data within the Biomedical Flows Simulation and Multiscale Modeling (BioSiMM) Lab. Core
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therapies, and disease models, including mutant mice and iPSC-derived neurons to further understand neurodegenerative processes. These projects are funded by multiple grants from the NIH. This opportunity
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epilepsies. They use a range of advanced genomic techniques including single-cell and spatial multiomic evaluation of epilepsy surgical tissue as well as iPSC-derived neural cultures and mouse models
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publications involving both traditional natural language processing techniques and large language models. Experience researching languages other than English would be a plus. This job will include opportunities
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 29 days ago
. Working with mouse models is preferred, but not required. Create a Job Match for Similar Jobs About The University of North Carolina at Chapel Hill Carolina rates among the nation’s great institutions
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candidate will contribute to cutting-edge research on the modeling, design, and performance analysis of emerging massive multiple-input multiple-output (MIMO) architectures for next-generation wireless
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& Responsibilities Plan, design and conduct advanced, multi-disciplinary research involving the use of mouse models, human tissue and blood samples. Use biochemical, cellular, molecular, and pharmacological approaches
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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(SynComs) that model complex environments (e.g. the human gut), for host–microbe interaction studies. Key Responsibilities Design and execute experiments to study the role of exRNA in synthetic microbial
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Spinoza's Theological-Political Treatise, this Schmidt Sciences-funded phase integrates large language models with computer vision to systematically uncover hidden networks of controversial printing during