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Scientist / Scientific assistant – Tree species detection using deep-learning Are you an ambitious data scientist with strong analytical and numerical skills, and expertise in geomatics, remote sensing, and
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learning, particularly deep learning and physics-informed methods, offer transformative opportunities to redesign how data are acquired and reconstructed, and how physiological parameters are inferred from
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. Project background We are excited to announce an interdisciplinary PhD opportunity focused on mechanochemical processes driving radical formation and redox cycling in the deep subsurface, with implications
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annotation, and emerging machine-learning and generative methods for spectra or structure proposals. Evaluate and test emerging technologies (hardware and software) in close interaction with collaborators and
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educates the next generation of structural engineers, equipping them with deep technical knowledge and top-level competencies in the use of timber as a high-quality building material, contributing
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Master’s degree in computer science, electrical engineering, or related discipline. Strong background in machine learning, deep learning, and optimization. Interest in cross-domain research linking ML
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to delivering state-of-the-art approaches to tackle the upcoming LISA data analysis challenges, exploring deep learning applications alongside traditional methods. Your responsibilities will include a combination
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that combine situated ethnographic detail with deep knowledge of cultural and political contexts and histories, ideally in a cross-national comparative perspective. You can learn more about the research