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aims to unravel how ecosystems function in all their complexity, and how they change due to natural processes and human activities. At its core lies an integrated systems approach to study biodiversity
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machine learning; acoustic signal processing enhanced by ML; human-in-the-loop/active-learning methods; representation learning; analysing sound sequences and vocal interactions; category discovery; Perma
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. The research is broadly focused on automated intervention design. An intervention is any external interference in an ongoing process that is performed to achieve a particular outcome. Being able to answer
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tasking in real time. The fellowship will investigate how mission planning can evolve from static scheduling to an adaptive, intelligence-driven process executed directly on board satellites. This includes
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experimental PhDs and Canon Production Printing to enable data-driven ink optimization. Why Join? Work in the multidisciplinary Processing and Performance of Materials group Work at the forefront of sustainable
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. What you bring We are looking for candidates with: A PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or a closely related field; A strong publication record in
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with engineered coastal defence structures, natural defences, single and multiple buildings with different characteristics founded on solid and mobile sediment beds. This rich dataset has only been
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the ambition to grow. For more information about LIACS, visit our website. What you bring We are looking for candidates with: A PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision
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million natural history objects, dedicated mentorship, and access to our broad international network, this position offers everything you need to thrive as a scientist. As a Postdoc You will find yourself
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on static process design and control that don’t account for feedstock variability, inefficient solvent use, and high energy demand, hindering scale-up. Information This project will be executed together