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) Domain Interest: A deep interest in seismology, induced seismicity, and the application of statistics and machine learning to earth science problems Analytical Skills: High-level quantitative skills and a
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, retrieval products) Strong programming skills in Python Experience in machine learning, ideally including deep learning architectures such as graph neural networks, transformers, or spatio-temporal models
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innovative methods to leverage machine learning for numerical weather forecasting and climate modeling. Project background We are looking for a motivated Machine Learning Scientist to join the development team
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Metropolitan Area, which combines the vibrant cultural scene of a modern European city with the convenient accessibility to its beautiful natural surroundings. They are also part of a deep-tech innovation
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years. Profile University degree (MSc or PhD) in data science, computer science, physics or a related field Experience in training and validating large-scale deep-learning models on distributed systems
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deep learning (segmentation and foundation-model architectures) Demonstrated ability to design and execute technical projects Scientific independence with clear interest in biological mechanisms and
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heating and cooling, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting
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deep learning workflows for tree species mapping. The position contributes to building a scalable system for forest monitoring by refining model performance and ensuring high quality geospatial data
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the vibrant cultural scene of a modern European city with the convenient accessibility to its beautiful natural surroundings. They are also part of a deep-tech innovation ecosystem through co-location with
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) Unsupervised machine learning and deep learning methods Analysis, visualization, and interpretation of learned design spaces Contributing to research outputs (prototypes, publications, open-source code) Profile