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, Bayesian methods) to behavioral and neural data. Assist with pharmacological and neuromodulation study protocols. Prepare and submit manuscripts for peer-reviewed publication. Contribute to grant writing and
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-informed / simulation-aware modeling Efficient algorithms for design-space exploration (e.g., surrogate modeling, Bayesian optimization, differentiable programming) Hybrid approaches combining data-driven
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of error-controlled biomechanical models in SOFA / FEniCSx / SOniCS for real-time use on AR devices Design of Bayesian neural-network surrogates and graph-based models for tissue deformation and brain shift
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to understand how these mutations disrupt healthy brain development, how brain changes relate to patient symptoms and how to best target treatment. This project seeks develop tools to better understand, diagnose
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, how brain changes relate to patient symptoms and how to best target treatment. This project seeks develop tools to better understand, diagnose, monitor and target in children with genetic epilepsies In
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Offer Description WITHIN THE FRAMEWORK OF THE PNRR PROJECT “Translational research targeting fibrosis regression, endothelial dysfunction reversal, and hepatocyte regeneration in advanced liver diseases
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. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in R and/or
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role The PhD is embedded in the national Climate Nexus Framework Convention with the target to boost climate readiness and global cooperation. The interdisciplinary research group by the Department
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the identification of new biomarkers and therapeutic targets for Parkinson’s disease, aimed at bridging the gap between fundamental research and clinical research by embedding the “bed to bench to bedside” research
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staff position within a Research Infrastructure? No Offer Description Subject description Forests play an important role in reaching the climate targets as they can act as natural carbon sinks. However