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applications to medical data. The project will combine theoretical work on multiparameter persistence with the design and implementation of algorithms for medical image analysis. The successful candidate will
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mathematical and computational engine of Artificial Intelligence (AI), and therefore it is a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world. Integreat
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analysing algorithms and metrics for ensuring fair treatment of groups defined by protected attributes (e.g. gender, age, ethnicity, …). Theoretical relations between fairness notions: studying
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(the final scope will be developed together with the candidate, and it is not necessary to cover all topics): Group fairness: developing and analysing algorithms and metrics for ensuring fair treatment
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circulation of radical content, how algorithms amplify or suppress such content, and how these dynamics can be regulated through policy and governance. The Research Fellow is expected to collaborate with
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technology management, or smart grids. Experience in development of mathematical meta-models, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno
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infrastructure and surrounding terrain. The work can include physics-based modelling of e.g., various events and wave propagation through different media, signal processing, large-scale data analysis, algorithm
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, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno-economic study, design and analysis of integrated systems. Experience with energy system
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Diego, USA). By bridging experimental neurophysiology with advanced algorithmic design, we aim to significantly enhance the understanding of high-dimensional neural activity patterns. The successful
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. Integreat develops theories, methods, models, and algorithms that combine data with general or domain-specific knowledge, helping lay the foundations for the next generation of machine learning. Integreat