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Field
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with deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. You have good programming skills in Python and experience with a deep learning framework such as
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 12 days ago
-series. Experience exploring machine learning and deep learning techniques for geospatial applications is highly desirable to effectively engage with Earth observation foundation models. Technical
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-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and functional evolution
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patients, generating new clinical multi-omics data and using deep learning, structural equation models, and causal inference to identify strategies for restoring immunological homeostasis. Together
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. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning
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earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc. Documented experience in machine learning, in particular deep generative
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UiO/Anders Lien 13th October 2026 Languages English English English Postdoctoral Research Fellow position in remote sensing and modeling of the cryosphere Apply for this job See advertisement About
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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of sedimentary basins, the deep crust-mantle system, and the evolution of different tectonic provinces. GEUS further has a core repository that stores cores and cuttings from Danish wells, and we have advanced
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working experiences, and professional mentorship and support. The postdoctoral associate will be affiliated with the Center for Avian Population Studies (CAPS, https://www.birds.cornell.edu/home/center