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that combine AI and traditional modeling and simulation (ModSim). You will work with cutting-edge exascale systems and novel AI hardware, collaborating closely with science application teams, academia, industry
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of the neural pathways involved in long-lasting behavioral recovery in a mouse model of Parkinson’s disease, and slice and in vivo physiological studies of changes in neural activity and synaptic connectivity
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prior research experience in one or more of the following areas: metabolism, single-cell transcriptome, 3D tissue or animal models, biomaterials, immunology, and stem cell biology. • Demonstrated
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with the use of laminar/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project
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). This project is centered on the study of ceramide-directed therapeutics for AML treatment. The position provides extensive training in preclinical cancer research, with a strong focus on mouse models
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 13 days ago
interested in this field of research and why you believe you would be a good fit for the Lai Lab. Education and Experience: Viral gene delivery, CAR-T therapies, computational protein modeling and design
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate
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) in the field of accelerator physics or a closely related science and engineering discipline Strong experience developing and applying computational modeling and simulation Familiarity with accelerator
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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aims to leverage AI, including transformers, CNNs, and foundational models, to resolve the remaining bottlenecks for scalable genome assembly. Responsibilities Responsibilities include, but are not