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include deep learning, reinforcement learning, differentiable modelling and inverse design. You will implement and evaluate these methods using experimental optical systems and work towards their
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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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. 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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according to the expertise of the applicant, and there is potential for fieldwork. You will have a PhD (or equivalent) in geoscience, with skills in, or willingness to learn, petrology and pore-scale flow
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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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, 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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with storytelling, popular culture, media, narrative change, representation, audience behaviour, social change or related fields. You will be an experienced researcher with a PhD or equivalent research
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for: Conducting research and development on AI-based solutions for automated defect inspection and condition assessment of train components by designing and developing deep learning, computer vision, and machine
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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
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access to the University's research resources, working under the mentorship and guidance of Associate Professor Courtney Fung, PhD, and Honorary Professor Bates Gill, PhD. The fellowship is structured