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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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) are required - Experience in working with Earth system model simulations is required - Experience in machine learning is required - Willingness to travel for work (project meetings, workshops, and research
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Max Planck Institute for Solar System Research, Göttingen | Gottingen, Niedersachsen | Germany | 23 days ago
of meteorites as well as numerical modeling on state-of-the-art supercomputers. We invite applications for a Postdoctoral Position (m/f/d) in machine learning for PDEs and turbulence control. Machine learning
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practical, effective, and scalable solutions. The two positions serve complementary but distinct functions within the project. One position focuses on the application of machine learning models for designing
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economic modeling, with interests including improved spatial resolution and machine-learning-enabled approaches for policy analysis. Postdoctoral Position (f/m/d) – Integrated Assessment Modeling (Climate
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the analysis of complex biomedical data using state-of-the-art AI and agentic system approaches, as well as the development of novel machine learning and deep learning algorithms. Your work will range from
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experimental data. Develop computational frameworks for integrating spatial and bulk multi-omics datasets. Create and apply statistical and machine learning models for feature extraction, data harmonisation, and
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | about 11 hours ago
-scale assimilation Develop new interfaces between PDAF and different data assimilation systems of project partners and to machine-learning based assimilation methods Extend the ensemble assimilation
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Imaging, Machine Learning, or a related field • Demonstrated research experience in generative models for medical imaging (e.g., diffusion models, VAEs, GANs) • Publications in high-ranking journals and
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. Antonio Scialdone’s group at Helmholtz Munich, a leading European hub for AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models