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Field
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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differential problems. 2) Development of adaptive mesh generation algorithms for distributed order fractional differential equations. 3) Analysis of the stability and convergence properties of the developed
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applications in the media change journalism, the public sphere, civic engagement and economic competition. We will develop and test new AI-applications to help solve problems such as disinformation, algorithmic
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AI-applications to help solve problems such as disinformation, algorithmic bias and filter bubbles. Together with our partners we will also (co)-create new ethical and legal frameworks for responsible
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learning algorithms and creating predictive models. This role will contribute to evidence-based processes and procedures for program quality review and assessment of outcomes data as well as develop
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data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms. Assist with organizing large-scale multimodal neuroimaging dataset, brain
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, engineering, or a related field. Strong programming skills and experience in machine learning or statistical modelling are essential. Experience with healthcare data, algorithmic fairness, or deep learning
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High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including unsupervised techniques, time-series modeling, and clustering algorithms
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advantage may arise. The focus of this project is to develop techniques that will enable gate-based quantum optimization algorithms to tackle realistic (large-scale, mixed-integer, and constrained) instances
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project MAREHC – “Maximizing the Renewable Energy Hosting Capacity of Distribution Networks”. Project Background The PNRR MAREHC project aims to strengthen the innovation capacity of the Romanian RDI system