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. Integrate deep mutational scanning data to assess viral fitness and immune escape, collaborating with experimental virologists. Work with large-scale genomic and proteomic datasets from BV-BRC, GISAID, and
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. This position offers an exciting opportunity to work at the intersection of HPC and AI, addressing critical communication bottlenecks and optimizing network interconnects for large-scale distributed systems
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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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requires not only expertise in LLMs and machine learning but also an understanding of the unique challenges posed by scientific data, which often includes large-scale numerical datasets, complex simulations
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or more of the following: Experience with large-scale molecular dynamics (MD) simulations using software such as LAMMPS. Experience in handling and data analysis generated from multi-million to multi
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of dynamical systems, which will be integrated into large-scale optimization frameworks to enhance the efficiency and reliability of power grid operations. The Postdoctoral Appointee will be responsible
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information Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork Desired skills, knowledge and abilities: Experience with large-scale molecular dynamics (MD) simulations
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diffraction, experience writing successful proposals for synchrotron experimental beam time at large scale facilities, x-ray data analysis expertise from single crystal materials. Knowledge of quantum systems
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for deployment on large-scale computing resources, such as high-performance computers (e.g., Perlmutter, Aurora, etc.). This includes tasks such as automating model design, optimizing hyperparameters, and training
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geometry manipulation with computer-aided design software. Experience with coupling CFD and FEA codes. Knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase/multiphase flow and