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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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data in any area of finance, such as asset pricing, machine learning, ESG investing, how social networks affect finance, research replicability, regulatory data in finance, financial institutions, and
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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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computer science. The candidate is expected to have solid knowledge in most of the following areas: Robotics Control theory Deep Learning & Machine learning Modelling and control of soft/continuum robots Experience
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Nordisk Foundation (NNF) New Exploratory Research and Discovery grant entitled: Information Theoretic Disentanglement of the Exceptional Biological Learning Machine, which is headed by Professor Jan
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of the following areas: Robotics Control theory Deep Learning & Machine learning Modelling and control of soft/continuum robots Experience with embedded systems programming (Preferably) with a strong background in
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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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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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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