The project focuses on enhancing the FAIR capabilities of SpatialData, a modern Python framework for spatial omics, across platforms, and on strengthening its language-agnostic file format based on the OME-NGFF open standard. * Expand the bridge between the scverse and R/Bioconductor ecosystems through interoperable software development focused on the OME-Zarr file format and the SpatialData framework * 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 * Marconato, L., Palla, G., Yamauchi, K.A. et al. SpatialData: an open and universal data framework for spatial omics. * Excellent framework conditions: state-of-t
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