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Kontogianni. Our research explores how intelligent systems can perceive, understand, and interact with the 3D world. We develop new methods in computer vision, machine learning, and multimodal 3D
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Computer Vision There is growing trend towards explainable AI (XAI) today. Opaque-box models with deep learning (DL) offer high accuracy but are not explainable due to which there can be problems in
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Biological Learning Machine, which is headed by Professor Jan Østergaard. The goal is to develop novel information-theoretic methods for identifying and analyzing temporal and spatial patterns of synergy and
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to have a strong interest in data analysis, and medical research, along with relevant academic background and skills within medical image analysis and machine learning that will enable them to contribute
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Compression of quantum data under unreliable entanglement assistance Joint compression and error correction for robust communication in the quantum-classical internet Quantum embeddings for machine learning
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within the broad topics of modelling tool-workpiece interaction in mechanical material removal processes, zero-defect manufacturing, machining system performance characterization as well as on-machine and
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undermine this future. Can you see how Machine Learning, Computer Vision, and Robotics can open up opportunities for autonomously operating agricultural robots? Are you passionate about making agriculture
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Job Description Are you passionate about brain-computer interfaces (BCIs), neurorehabilitation, and intelligent assistive technologies? The Department of Health Technology (DTU Health Tech
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(EoS), or machine learning approaches. Hands-on experience in extracting bioactive compounds from biomass. Strong collaboration skills and the ability to work effectively in interdisciplinary teams. A
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user