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various components of the hydrological cycle (such as precipitation, evapotranspiration, runoff, and infiltration), forms the core of traditional hydrological modeling approaches. Although powerful
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of rural communities toward resilient, inclusive and climate-smart business models. The focus of this PhD will be on modelling and assessing the sustainability and macroeconomic implications
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Description Water can move in two interconnected realms: the fast, visible rivers at the surface and the slower, pressure-driven flow within substrates. Today, engineers can model each realm
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nano-structures. In this project, we will combine numerical models, experiments, and artificial intelligence (AI) to guide the design of specific DNA nanoconstructs. The primary goal is to build an AI
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the change! We offer ideal conditions for you to complete your doctoral degree: Competent and interdisciplinary working environment, as well as an excellent framework in the areas of experiments and modelling
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to facilitate a rapid and efficient exchange among experimental and computational groups and Devise an approach in invertible predictive modelling that links semiconductor properties to the composition of lead
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sciences a pleasant and respectful working atmosphere integration into a successful and committed team in an international environment and network flexible and family-friendly working time models and the
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learning (ML) methods—including surrogate modeling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open source OptiMic software) Perform descriptor
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-management and conservation practices": PhD student (f,m,div) in the Field of Geodata, Nitrogen and Soil Parameter Modelling Reference number: 18/2025/4 The salary will be based on qualification and research
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to investigate mechanical properties under application-relevant conditions Microstructural characterization using advanced optical and electron microscopy techniques Analysis and correlation of mechanical behavior