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Field
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unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many
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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker
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unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many
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robotic systems and AI models. You will learn how to programme advanced robotic systems and how to implement aspects of deep learning and neural networks for chemical property prediction. You will be part
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- and electronics- workshops, but also with the NanoLab Amsterdam cleanroom facility situated in the neighboring NWO-institute AMOLF; develop deep-rooted expertise with and maintenance of WZI’s research
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. (or equivalent) in Computer Science or a related discipline ML expertise: You have strong programming and deep learning experience (e.g., PyTorch, TensorFlow), backed by a substantial project (thesis, paper, etc
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agents. Your research will explore how reinforcement learning, multi-agent cooperation and generative worldmodels can deliver adaptive strategies that thrive amid volatile, multi-asset markets, micro
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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cleanroom facility situated in the neighboring NWO-institute AMOLF; develop deep-rooted expertise with and maintenance of WZI’s research facilities and software packages; be able to act as advisor and expert
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scholarship is suitable for students with a background in Engineering, Mathematics, and Computer Science. Students with interests in machine learning, deep learning, AI, intelligent decision making