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for Computational Sciences. They will collaborate with leading computer and computational scientists at ORNL and external collaborators in the development and application of new computational techniques specifically
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members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience in sampling algorithms and related areas
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their data pipeline. These foundation models will be used to improve the operation of cryoSTEM microscopes when applied to biological samples. Parallel Model Training: Deploy parallel computing systems
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used to improve the operation of cryoSTEM microscopes when applied to biological samples. Parallel Model Training: Deploy parallel computing systems for training large-scale cryoSTEM foundation models
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empirical evidence. The project runs for 33 months and comprises three research components operating in parallel, each led by a postdoctoral researcher and integrated through monthly team meetings and
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Model. In parallel, the group is engaged in hardware activities related to the development and implementation of new FPGA-based trigger processors, both for the operation of the ATLAS muon spectrometer
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: Understanding of the principles of atmospheric modeling, particularly using WRF-Chem. Knowledge of the physical and chemical processes simulated in the model, including cloud microphysics and interactions with
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, Sweden during the time of your employment. You are able to work independently and communicate progress / issues clearly. You are proactive and can manage working in parallel projects if need be. You have
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of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing. Author peer reviewed papers for internal and external release as
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systematically with several stakeholders and parallel activities. The following experience is particularly advantageous: Implementation research and use of established frameworks, particularly PRISM and/or RE-AIM