Software development skills in Python and familiarity with the data science and geospatial Python stack (e.g., xarray, dask; GeoPandas/Shapely a plus) * Open-source development practices: Git/GitHub, code review, testing (e.g., pytest), packaging, CI/CD (e.g., GitHub Actions), and documentation (e.g., Sphinx) * Research experience implementing statistical learning or machine learning (e.g., Bayesian inference, deep learning), ideally connected to spatial omics, and experience with frameworks like PyTorch, Keras, Pyro, or TensorFlow
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