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areas of statistics, data science, and artificial intelligence (AI). The position will focus on developing and applying novel statistical, machine-learning, and AI methods to advance biomedical and
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, and construction progress analysis. Key Responsibilities: Conduct research in computer vision, machine learning, large language models, AI agents, and 3D reconstruction for infrastructure digitalization
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multivariate, statistical-genetic or machine-learning approaches. · Research involving developmental, ageing or neuropsychiatric cohorts, including longitudinal or large-scale population datasets. · High
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preferred skill, including GIS, spatial suitability modeling, multi-criteria decision methods, and machine-learning-based feature importance. Exposure to resource questions relevant to large-load development
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. The analyses will include large-scale epidemiological health data as well as daily life activity data collected using wearable sensors, with a strong emphasis on the application of machine learning methods. In
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-scale inverse problems by combining interpretable high-level probabilistic models, multi-physics data integration, and modern machine learning. The resulting methods will be validated on groundwater
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/ESA_selects_Harmony_as_tenth_Earth_Explorer_mission ) The candidate will develop and apply cutting-edge remote sensing or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via
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, including parsing and processing large document corpora. Strong understanding of machine learning or AI methods applied to health or biomedical data. Demonstrated ability to assess model outputs, identify
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. Specific topics of focus include, but are not limited to, linear response, statistical limit laws, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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machines Machine Culture Ongoing work on social reinforcement learning and evolutionary optimization of social strategies Our aim is to advance the scientific knowledge of human-AI systems by understanding