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and/or Matlab, parallel programming Experience in international collaboration Fluent in English (spoken and written) Demonstrated ability to publish in international journals and present at conferences
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)—topics including, but not limited to: · Physics-informed neural networks (PINN) & neural operators · Physics-aware convolutional neural networks (PARC) · Meta-learning/transfer
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Language Model (LLM) and their use in Model-Driven Engineering (MDE) and software architecture Demonstrated proficiency in MDE, including expertise in modeling, meta-modeling, model transformations, and
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Postdoctoral Position in the Yale Cancer Prevention and Control Training Program The Yale School of Public Health and the Yale Comprehensive Cancer Center (YCC) have a Cancer Prevention and Control
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration
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Type Staff Job Description Major/Essential Duties of Job: Conduct ecohydrological field studies to determine soil infiltrability, soil water dynamics, tree transpiration. Designing and implementing meta
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be considered an asset Proven record in publication
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, excursions) Global geo-/paleomagnetic field modelling expertise is advantageous Strong programming skills, preferably in Fortran, Python and/or Matlab, parallel programming Experience in international
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-00368-1 ) or executing large-scale meta-analyses of mass spectrometric datasets (https://www.nature.com/articles/s41592-021-01194-4 ). The research is computational in nature but involves close