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computationally challenging. To address this, our research employs advanced computational methods to simplify high-fidelity 1-D hydrodynamic models based on Partial Differential Equations (PDEs). This approach
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approaches are integrated with behavioural studies, large-scale data analysis, biophysical modelling, multi-omic profiling, and collaborative human fMRI studies to build a comprehensive understanding of brain
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NORPOD is a collaborative postdoctoral program of the Nordic EMBL Partnership for Molecular Medicine . The partnership is a network of four national research centers across the Nordics and the
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experiments with quantitative microscopical analysis and physics-based modelling to understand how conifers solve the challenge of solute transport against the flow of water through the needle, and what
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`liquid-liquid phase separation' (LLPS). We will use programmable, multi-component model systems of biomolecular phase separation to investigate the transport of biomolecular information, stress, and light
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
Area of research: Scientific / postdoctoral posts Starting date: 01.07.2025 Job description: Postdoc (f/m/d): Machine Learning for Materials Modeling With cutting-edge research in the fields
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are seeking a motivated and enthusiastic colleague with strong computational skills in the analyses of complex data sets to join our teams. About the project We have generated advanced brain on chip models
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Work group: Institute of Coastal System Analysis and Modeling Area of research: Scientific / postdoctoral posts Starting date: 21.05.2025 Job description: Postdoc position in the field ofclimate
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learning, deep learning, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific
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mining, statistical analysis, and computational modeling; Experience with programming languages such as Python, R or similar, as well as familiarity with relevant libraries and frameworks (e.g., TensorFlow