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based on Machine Learning (ML) emulators have taken the weather predictions research by storm, as they run faster and use less energy than traditional approaches: numerical models based on physical
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 12 days ago
Job description:Postdoctoral Researcher (f/m/d) in Machine Learning and Surrogate Modeling for Geochemical Systems With cutting-edge research in the fields of ENERGY, HEALTH and MATTER, around 1,500
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validate adaptive mechanisms for LoRaWAN based on machine learning techniques, targeting improved reliability and energy efficiency in mobile scenarios. To achieve this, it is necessary to go beyond
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Zanna, the successful candidate will focus on developing generative machine learning models for complex dynamical systems for probabilistic forecasts. The postdoc will be expected to lead independent
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based on the new data generated, incorporating key variables identified in (i), and use statistical and machine learning methodologies to ensure high predictive accuracy and robustness; iii) validation
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: Education: Ph.D. in machine learning, computer science, engineering, physical science or related technical discipline. Experience: Expertise in developing and training AI models Proficiency in Python
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integration, metadata harmonization, preprocessing, and quality control of large public sequencing datasets Implement and benchmark machine-learning models for predicting biological and ecological metadata from
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, and machine learning models for functional genomics research in mycobacteria. Responsibilities Responsibilities include: Develop and maintain Django-based web applications and databases for sharing
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users, thanks to the use of machine learning tools and techno-economic analyses. This project is aligned with the sustainable development goals (SDG) 7 and 10 of the United Nations, by promoting a low
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models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available