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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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in the genome, including changes in genetic diversity and in how genetic variation is distributed within and among populations. These signatures are not uniform along the genome, but depend on local
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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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when humans impose strong and sustained new selection pressures? Evolution depends on the genetic variation already present in populations, yet strong selection can rapidly reshape that variation
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responded to past climatic warming at the Pleistocene–Holocene transition. Range expansions and demographic change leave signatures in the genome, including changes in genetic diversity and in how genetic
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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