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the position of: Lecturer for the Module M05 'Computer Vision & Perception' 10 ECTS (18%) Start date: August 1st, 2027 Module completion: each autumn semester (beginning of August to end of January) Description
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PhD Studentship: Efficient Long-Horizon Task Execution in Physical AI (deep learning, computer vision, robotics) Number of awards: 1 Award information: Fully funded PhD studentship covering Home
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Dipartimento di Ingegneria dell'Informazione - Università degli studi di Padova | Italy | 3 months ago
Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The goal
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for data-efficient vision foundation models. Foundation models in computer vision currently rely on massive datasets and brute-force scaling. This leads to high data requirements, hidden biases, limited
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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. Postdoc position in 3D Computer Vision, remote sensing ... The Laboratory of Visual Intelligence
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of Engineering programme as Associate Professor in AI and Computer Vision. You will play a central role in educating future engineers, combining deep technical expertise with close interaction with students in a
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Artificial Intelligence and Data Science, with a focus on computer vision. The successful candidate will bring expertise in one or more of the following areas: Vision-Language Models and Multimodality
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gained to improve the sustainability of agriculture and the climate change resilience of crops. We are currently looking for an expert in Computer Vision, Image Analysis and AI to join our team. In
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applications for the position of Research Fellow . The Research Fellow (RF) will play a key role in advancing the research mission of NTU by supporting cutting-edge projects in computer vision, machine learning
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performance (10–50 Hz) on hardware with constrained memory, power and compute. The project will investigate training, adapting and deploying compact Physical AI models efficiently. Work may cover vision