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the Electrical Energy Systems group, collaborating closely with PhD students, postdocs, and process and materials partners in a supportive, multidisciplinary team. What you will deliver A quantitative
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combine research in Remote Sensing and AI with teaching and student supervision? Have you recently earned a PhD in Remote Sensing, or are you about to finish your PhD, and feel inspired to share your
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-disciplinary domain. Specific research topics to apply to bioacoustics might include: low-footprint machine learning; acoustic signal processing enhanced by ML; human-in-the-loop/active-learning methods
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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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or cryo-ET sample preparation, data collection and image processing is particularly valuable. You are interested in understanding molecular mechanisms of host–pathogen interactions and are willing
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in close collaboration with Sjoerd Dirksen , Johannes Maly , and a PhD student that will start at the Ludwig-Maximilians-Universität (LMU) in Munich, Germany. During the project, you will spend two
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identify those with the highest potential for early adoption in space engineering; translate theoretical advances into algorithmic innovations, design principles and prototype tools that can be integrated
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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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ranging from (bio)chemistry and physics to bioinformatics and cell biology. Our drive is to push boundaries in our aim to visualise molecular processes in their native context and at the highest possible
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eager to combine computer vision, biomechanics, and synthetic data generation to build tools that will shape the future of home-based rehabilitation monitoring. You bring: A PhD in Biomechanical