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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM
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discipline. Preferred Qualifications: Familiarity with scientific-data tools and formats such as xarray, Dask, NetCDF, or Zarr. Experience with GPU computing, distributed training, high-performance
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. Experience with xarray and scalable scientific-data workflows using Dask and NetCDF or Zarr datasets. Experience with GPU or distributed training and familiarity with agricultural or environmental
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with a commonly used machine-learning framework or library, such as PyTorch, JAX, TensorFlow, or scikit-learn. Experience with research software, large-scale experimentation, GPU computing, high
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, economics, political science, social research, engineering, and the humanities; High-performance computing, GPU-enabled workflows, large-scale data processing, research storage, and technical support