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central scientific challenge will be to learn integrated representations of forest ecosystems from datasets with very different characteristics, resolutions, coverage, and levels of supervision
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requirements for admission to the PhD programme Experience implementing and modifying deep learning architectures. Working knowledge of Python and a modern deep learning framework. Strong programming skills in
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degree in Computer Science, Artificial Intelligence, Robotics, Electrical Engineering, or a closely related field. A strong academic record and solid background in machine learning and deep learning
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, computational biology, statistics or a closely related field. You have strong programming skills, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision
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, visiting researchers, master's students, etc.) Research Context Recent advances in mobile robotics have been driven by remarkable progress in perception, deep learning, and control. However, current robotic
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opportunities for innovation and support well-founded decision-making within the programme. You design and organise engaging workshops and learning experiences for students, connecting digital and technological
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” modeling solutions; we are open to and excited about applying all different types of statistical and ML techniques, from linear models to deep learning, depending on what best fits a given problem. The most
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and support well-founded decision-making within the programme. You design and organise engaging workshops and learning experiences for students, connecting digital and technological developments to real
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archaeological signatures (e.g., micro-relief, edge structures, etc.) – Design and implementation of new deep learning architectures (both supervised and unsupervised/few-shot, 2D and 3D) for an efficient and
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to the development of sustainable materials for the hydrogen economy. You are an independent thinker, eager to learn new experimental techniques, and enjoy collaborating with researchers from different disciplines as