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generative models Is highly proficient in Python, PyTorch and/or JAX Has experience training large-scale neural networks on HPC or GPU clusters Has experience with representation learning and sequence or
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applications. The position includes: Developing batch-processing workflows for MSI and metabolomics data, including normalization, quantification and feature alignment. Developing spatial segmentation
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attracting external research funding, and a demonstrated ability to work across disciplines. The candidate will be attached to both the DDC and DIAS and should be prepared to engage in multiple and
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resilience across land-use types. To this purpose, the Postdoc will be expected to combine multiple remote sensing techniques across scales, including Sentinel-2, Landsat or Sentinel-1. Compared to many
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multiple remote sensing techniques across scales, including Sentinel-2, Landsat or Sentinel-1. Compared to many land surface phenology studies at broad spatial scales (e.g. kilometers), the aim of
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multiple democracies. For Postdoc 1 , we particularly look for: Documented experience with computational text analysis, including natural language processing and the use of (multilingual) large language