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and deep understanding of machine learning, artificial intelligence, algorithms, and knowledge of the latest developments in AI. Proficiency in ML tracking/monitoring tools (MLflow, Grafana) and LLM
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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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Knowledge in the field of Machine Learning, including training, inference, and optimisation of transformer architectures Knowledge in the field of ML security is desirable. Good Python skills, especially with
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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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: 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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explicit model of the biophysical effect of land use change, a machine learning emulation of dynamic global vegetation models. Both activities aim to improve understanding and quantification of the effects
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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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of radiance data from new hyperspectral infrared instruments such as IASI-NG, MTG-IRS Enhancement of CrIS radiance assimilation algorithm are highly encouraged. - Use machine learning methods to cope with model
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want to grow into strong engineers and researchers in either: data & systems for high-frequency pipelines, and/or machine learning models, infrastructure and experimentation Strong fundamentals
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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