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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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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
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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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place of work is Paleoceanography and Paleoclimate Group, Department of Geoscience, Aarhus University, Høegh-Guldbergs Gade 2, DK-8000 Aarhus C, Denmark: https://geo.au.dk/paleo Contact information
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that include collaboration partners from both academia and industry. Further information For information about the CAPE research center, please see https://www.sdu.dk/en/forskning/cape For
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accompanying families, including relocation service and career counselling to expat partners. Please find more information here: https://internationalstaff.au.dk/relocationservice/ Please find more
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to expat partners. Please find more information here: https://internationalstaff.au.dk/relocationservice/ Please find more information about research opportunities at Aarhus University here: http