-
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
-
, 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
-
. Implement conformal prediction and uncertainty quantification techniques to provide reliable risk assessments and uncertainty estimates in LLM applications. Present research findings at national and
-
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
-
, 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
-
. 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
-
, and spatial transcriptomics. Key responsibilities include: Developing AI/ML methods for image alignment across modalities Automated feature detection Predictive modeling of vascularization patterns
-
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