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to imagine novel task configurations and learn robust manipulation policies from just a few real demonstrations. You will work at the intersection of 3D computer vision, physical simulation, and robot learning
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feed into this vision. The intended start date is July–August 2026. Job requirements PhD in machine learning, artificial intelligence, computational chemistry, computational materials science, or a
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applicants should have a strong academic record with a solid background in Machine Learning. Knowledge of Vision-Language-Action models and Novel View Synthesis techniques is a strong plus. Good programming
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Mathematics (Inverse Problems), Computer Science (Machine Learning, Computer Vision, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of
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multimodal data integration across organismal domains and data modalities, making use of state-of-the-art methodologies such as systems/network analysis, artificial intelligence and machine learning and/or
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or computer vision); A strong scientific track record, documented by publications at first-tier conferences and journals (e.g., NeurIPS, ACL, ICLR, EMNLP, NAACL or COLM); Have excellent programming skills; Have
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parameters based on paired visible-light and X-ray images. The developed techniques will be validated on real data. As a candidate, you must have a strong background in machine learning and computer vision, as
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of receiving their PhD. In particular for this position, the following is required: PhD in data science, AI, computer science, machine learning, Earth system science, climate etc., with a thesis subject relevant
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in the new Lab42 building at the Amsterdam Science Park. The VIS Lab performs research on deep learning and computer vision, from hyperbolic learning to medical imaging and from NeuroAI to foundation
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, or related background. Strong background in machine learning, computer vision, and deep learning. Knowledge of transformer architectures and foundation models. Experience with few-shot learning, self