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Field
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computing environments and GPU computing. Proven experience in weather and climate models development and applications. Experience in machine learning, deep learning, or AI applications for atmospheric
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Location: The Francis Crick Institute, London Short summary We are seeking an ambitious Postdoctoral Fellow to develop the next generation of deep mechanistic models (DMMs; Fabrini & Fröhlich
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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involves development of deep learning based synthetic data generators that obtain both good utility and protection of privacy, through tailored model approximation, as well as new measures of privacy and
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research. Key Responsibilities: Develop, implement, and optimize machine learning/deep learning models for digital pathology image analysis Analyze large-scale histopathology, omics, and clinical datasets
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architecture trait identification. The overall objectives of the project include: Deep understanding of plant water relations to extreme environmental stresses. Hands-on experience in measuring leaf gas exchange
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reservoir-scale heterogeneity and mineralogical variability in deep-marine lobe successions, and the role these may play in CO2 migration, pressure dissipation, and reaction front surface area to support CO2
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related field). Decent programming skills, especially in Python or JAX. Familiarity with finance theory (asset pricing, derivative pricing, risk management etc.). Familiarity with machine learning or deep
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. You will develop and apply state-of-the-art deep learning methods for land cover classification and change detection using multi-source aerial and satellite imagery. Working within an interdisciplinary
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mentorship. Develop, implement, train, and validate machine learning and deep learning models for AI-driven prediction of cell physiology, metabolism, and functional behavior, with input from the research team