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). Information Key Responsibilities: Develop a generalizable and explainable (gray-box) model for adaptive patient monitoring. Utilize a mixed approach combining real and synthetic data for algorithm development
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Application deadline: 15 March 2026 Apply now This position focuses on fundamental open problems in algorithm design and computational complexity. The main theme will be a unifying theory of algorithmic power
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by developing a general mathematical theory of symmetries in BP and efficient algorithms for symmetry detection and exploitation. While the related field of mixed-integer programming (MIP) primarily
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processing algorithms, build a Dash-based GUI, and develop standardized analysis pipelines. The role includes community-oriented tasks such as documentation, tutorials, and user support. The work requires
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(algorithms), and statistics. During this project, you will develop new methods to construct phylogenetic networks and generalize mathematical frameworks of phylogenetic network classes to tackle related
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to contribute to the development of innovative EO solutions. We offer: a stimulating multinational, interdisciplinary and open work environment; access to high-performance computing infrastructure and
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developing algorithms for monitoring and assessing perception and motor behaviour in the hospital and home setting, with an emphasis on sensorimotor integration within the perception-action cycle. Patient
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are transparent, interpretable and aligned with human needs and values. You will focus on developing, testing and reviewing a methodology to make AI systems transparent and explainable. The goal is to empower
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that enable material traceability and circularity in plastics. The role focuses on developing and curating Deep-UV spectral databases, designing AI-based classification models, and further advancing
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. This evolution calls for new methodologies capable of effectively representing and compressing data in infinite-dimensional settings. In this project, we aim to address this challenge by developing a theoretical