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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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learning (“buddying”) Both roles will contribute to a developing cross-disciplinary Deep End research community Developing Skills & Expertise: Develop academic skills around research from design to delivery
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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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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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. 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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, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep Learning Video Understanding Multimodal AI Excellent programming skills in
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. We particularly welcome applicants with strong expertise in Isabelle/HOL or other interactive theorem provers, formal verification and security, or neurosymbolic AI and AI-assisted reasoning. Deep
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. We particularly welcome applicants with strong expertise in Isabelle/HOL or other interactive theorem provers, formal verification and security, or neurosymbolic AI and AI-assisted reasoning. Deep
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thinking and a deep interest in the relationship between storytelling, popular culture and social change. You will be confident designing and managing your own research project, communicating complex ideas
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projects Demonstratable experience of applying agentic AI and sequence-to-signal deep learning modelling Demonstratable experience generating Nextflow Pipelines Desirable criteria Demonstratable experience