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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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Postdoctoral Position in Probabilistic Machine Learning for Spatio-Temporal Data Modelling A postdoctoral position is available at the Department of Computer Science, Aalborg University Copenhagen
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strong background in machine learning, computer vision, or data-driven modeling. You have extensive experience in the development and implementation of AI and machine learning algorithms, ideally with
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University Medical Center Utrecht (UMC Utrecht); yesterday published | Netherlands | about 2 months ago
This is a full-time post-doctoral position (1.0 FTE), initially for 1.5 years, and will be embedded within the UMC Utrecht AI-Lab initiative. Your primary focus will be the development of AI models
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). This position focuses on the machine learning methodology of the project, aiming to: Develop probabilistic spatio-temporal models that integrate uncertainty from climate projections into land-use forecasts
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machine learning models that predict soil health and crop performance. The position will exploit datasets integrating biochemical and molecular soil parameters (with a focus on microbiome features from
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We are seeking to appoint a Senior Postdoctoral Researcher in Statistical Machine Learning and Deep Generative Modelling to apply and develop cutting-edge deep generative probabilistic models
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the Oxford–Novartis Collaboration for AI in Medicine. You must hold a PhD/DPhil in Statistics, Statistical Machine Learning, Deep Generative Modelling, or a closely related field, together with relevant
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, transfer learning or probilistic methods This would evaluate and potentially enhance the usability of deep learning models for complex tabular data with mixed continuous and categorical columns with non