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As a postdoctoral researcher, your primary responsibilities will be: Develop machine learning and deep learning models, with a strong focus on computer vision, for the characterisation and
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and apply AI and machine learning methods for signal processing, image analysis, data fusion, and prediction; · build physics-informed and hybrid AI models that combine geophysical knowledge with data
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expertise in specialised domains where labelled data is scarce. Recent foundation models have transformed computer vision, yet their ability to acquire new expertise remains limited when training data is
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vision, yet their ability to acquire new expertise remains limited when training data is scarce or specialised. This project aims to develop the next generation of adaptive visual learning systems by
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across a range of application areas, including education and healthcare. As these systems are increasingly deployed in high-stakes environments, there is a growing need for machine learning models
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inference), and automated machine learning. holding (or close to acquiring) a PhD degree in Computer Science, Artificial Intelligence or a closely related field; strong research vision and an academic mindset
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We are seeking a postdoctoral researcher with a curiosity-driven record who works at the intersection of machine learning (ML) and the sounds of wildlife (“bioacoustics”). We are also happy
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, navigation and control, visual landing and event-based vision, scientific deep learning for physical systems, spiking neural networks for event-based systems. Building on this experience, the research line
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energised — not deterred — by problems that sit between physics, learning and the messy real world. Your experience and profile: a PhD (completed or near completion) in Machine Learning, Computer Vision
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the intersection of machine learning (ML) and the sounds of wildlife (“bioacoustics”). We are also happy to consider candidates in one of the two fields who can demonstrate a strong basis for working in this cross