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written and spoken. Experience with experimental fluid mechanics and computer vision is an advantage. Our offer We offer a stimulating, multidisciplinary research environment within the ETH Domain, with
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that support the unit for area protection and marine spatial planning, as well as operations at SLU Aqua. Your profile You have documented expertise in marine ecology and computer vision and machine learning
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. Project proposals should assume utilisation of data from Crowdcube, Seedrs, and firm-level sources such as Companies House and Beauhurst, and consider use of natural language processing, computer vision
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by developing AI methods that improve recognition of rare species while providing reliable measures of uncertainty. Using state-of-the-art computer vision approaches — vision transformers, self
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EPSRC ReNU+ CDT PhD Studentship: Physics-informed machine learning for deep geothermal systems under uncertainty. Award Summary 100% fees covered, and a minimum tax-free annual living allowance
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Science. Commitment to undergraduate and graduate education. Demonstrated expertise in machine learning/deep learning and software development (Python; PyTorch/TensorFlow). Peer-reviewed publications and strong
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. Your profile You have documented expertise in marine ecology and computer vision and machine learning methods for video-based fish monitoring. You have excellent IT skills and experience in handling
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models, multi-view computer vision, semantic graph-based representations, and self-supervised learning—to automatically interpret and understand complex surgical procedures. The overarching goal is to
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computing. These include computer graphics and geometry processing, computer vision and image processing, visualization and visual analytics, augmented and virtual reality, machine learning and deep learning
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, intelligent vehicles / robotics, acoustics and signal processing, computer vision. Demonstratable experience in applying Deep Learning, using PyTorch, TensorFlow, JAX on real-world sensor data. Experience with