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research will involve synergistic collaborations with a multidisciplinary team involving engine modelers, computational fluid dynamics (CFD) experts, and computational scientists to enhance the predictive
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, the appointee will help ALCF enhance the performance of AI-driven applications and HPC workloads, ensuring efficient utilization of resources and improved system predictability. Position Requirements Required
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ensembles of models. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of experimental particle physics Programming expertise in C/C++, Python, Fortran, or another
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candidate will work on cutting-edge research integrating genome-scale language models (GenSLMs) with deep mutational scanning data, and experimental virology to predict viral evolution and identify emerging
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. Implement conformal prediction and uncertainty quantification techniques to provide reliable risk assessments and uncertainty estimates in LLM applications. Present research findings at national and
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machine learning expertise, with the goal to enhance predictive capability and scalability of multi-scale and multi-physics simulation codes. Perform high-fidelity CFD simulations of complex physical
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, and optimize for energy efficiency HPC applications and high performance data stream analytics workloads. Use of novel accelerator designs, and automatic methods to model/predict how performance would
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. The position focuses on analyzing convective systems and weather extremes to understand their predictability and how they are influenced by environmental factors such as urbanization, aerosols, and climate
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, and spatial transcriptomics. Key responsibilities include: Developing AI/ML methods for image alignment across modalities Automated feature detection Predictive modeling of vascularization patterns
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the goal to enhance predictive capability and scalability of multi-scale and multi-physics simulation codes. The prospective postdoctoral appointee will perform multi-physics and multi-scale CFD simulations