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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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on the development of AI models for analysis of cardiac CT scans, with the aim to explore how machine learning models can quantify cardiovascular disease and predict future events from CT scans. The project will
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mass spectrometry and machine learning now allow us to unravel this “dark proteome.” This position aims to use state-of-the-art AI-guided proteomics and systems biology approaches to map protease
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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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or interest in runtime reconfiguration techniques and system safety considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an
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Job Description If you are ambitious and interested in joining a supportive and dynamic research team working with Operations Research and Machine Learning on an important application look no
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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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Job Description The Machine Learning in Photonic Systems group at DTU Electro at the Technical University of Denmark is seeking a candidate for a PhD position to research multiplexing in photonic
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biology, or analytical workflows. Interest in single-cell analysis, cancer biology, and translational research. Basic level expertise in computational biology (e.g., bioinformatics, machine learning), with