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, the identification of predictive features, and the construction and validation of statistical or machine-learning-based models. The postdoctoral researcher will be responsible for: Developing a
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rapidly evolving areas such as autonomous systems, data-driven modeling, learning-based control, optimization, complex networks, and sensor fusion. Research at the division is characterized by close
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. Designing and implementing semantic mappings from external metadata standards, ontologies, and controlled vocabularies into the HelmholtzKG data model. Building and operating automated data pipelines
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, affect, and cognitive control — and how disruptions in these processes underlie psychiatric disorders. We use a multimodal approach combining fMRI, pharmacological manipulations, computational modeling
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the GEOSIC project, based on research in geopolitics. We will implement the scenarios developed for the SAI deployment in a climate model coupled with a control module (PID) in order to quantitatively simulate
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-based models that optimise and control pharmaceutical manufacturing processes effectively. Your main responsibility is to develop and enhance discrete element models (DEM), integrating physics-based
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data, integrating multiple data sources, and supporting analytical modelling that informs infrastructure planning and policy. The position emphasises robust data engineering practices, reproducible
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environmental geophysics. This PhD project aims to advance the process-based understanding of SSF by combining state-of-the-art geophysical methods with controlled field experiments and numerical modeling
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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: design and implement technologies and algorithms (including feedback control design and implementation) to reduce the noise sources that limit the current detector sensitivity; model optical subsystems