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and interpretation. Prominent examples include time sequences on groups and manifolds, time sequences of graphs, and graph signals. The objectives The project aims to develop unsupervised online CPD
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innovative ideas to enhance existing algorithms and frameworks. Analyse and interpret findings, ensuring scientific rigour and practical relevance. Prepare and submit manuscripts to leading conferences and
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This is a Research Assistant / Research Associate position within a 4-year long project. This position involves the development of novel Computer Vision algorithms for embedding computer vision
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interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific computing and machine learning. The research will emphasize both theoretical
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the development of more efficient online learning algorithms for manifold-valued data streams, with an initial focus on change-point detection, opening the door to new unsupervised data exploration methods. Next
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suitable data models [CSC+23]. Objectives As far as the design of efficient numerical algorithms in an off-the-grid setting is concerned, the problem is challenging, since the optimization is defined in
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Accelerated AI Algorithms for Data-Driven Discovery (A3D3; https://a3d3.ai). We are seeking a post-baccalaureate research fellow (Postbac Researcher) to join our interdisciplinary team of scientists and
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: · Coordinating between classical and quantum algorithms and approaches. · Developing or using innovative quantum algorithms for applications or design. · Efficient algorithms for emulation of quantum
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Information Engineering and Media to develop Machine Learning and AI algorithms for real-world and media-related applications. The Research Associate/Research Fellow is also expected to support teaching
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closely related field focusing on artificial intelligence applications; (b) advanced proficiency in programming with Python, MATLAB or R; and (c) experience in developing algorithms for remote sensing