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development Programming in Python, R, MATLAB, or related scientific computing environments Reproducible research practices, including version control, high-performance computing, and open science
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Health Initiative), and local Stanford data. Lab members have access to state-of-the-art computational resources, including cloud-based computing and Stanford high performance clusters. We offer
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‑performance computing cluster offer an unmatched playground for ambitious science. Key Responsibilities • Design, execute, and analyse high‑density electrophysiological recordings (e.g., Neuropixels) in
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: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling, representational analysis, or
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of the following areas: atmospheric convection, radiative transfer, climate change, atmospheric/fluid dynamics, or machine learning. Proficiency in numerical analysis and high-performance computing
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-performance computing, and Stanford's broader ecosystem of core facilities. Required Qualifications: Ph.D. in structural biology, proteomics, biochemistry, life sciences, or a related discipline
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informatics. We provide access to high-performance computing, secure data environments, clinical imaging data, and software tools (Python, PyTorch/TensorFlow, radiomics, 3D Slicer, DICOM tooling). We
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segmentation, feature extraction, signal quality assessment, artifact detection, or time-series modeling. Experience with Python scientific computing tools such as NumPy, SciPy, pandas, scikit-learn
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management. Experience working comfortably at scale: large and unwieldy data frames, high-performance computing clusters or cloud compute, and workflows that must run reliably on a fixed weekly