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neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be applied in close
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planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and manipulation strategy adaptation Real-world
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, extension is sought) Contract:TV-L Your tasks Molecular laboratory work with 2D and 3D cell culture models Experimental design, data analysis and interpretation Publishing research findings in peer-reviewed
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the project include mathematical derivation, analysis, and comparison of models, methods, and simulation approaches; rapid prototyping of new ideas in custom code; implementation of new models, methods, and
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and physiological function of specific transport proteins in heterologous expression systems in crops and (trans-genic) model plants. • A unique set of Arabidopsis and barley transporter “mutants
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of fluid dynamic modeling and the analysis of thermomechanical stresses, you will accompany the entire development process – from the optimization of electrochemical performance to the elaboration
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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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culturing, integrating multiple automated subsystems with image-based machine learning models. Our objective is to enable robotic decision-making through machine learning, paving the way for a standardized
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on skills and interest): Build “incentive-preserving prediction models” for variables with positive global externalities, based on country characteristics (GDP, population density…) Develop procedures
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Excellent skills in statistical modelling, preferably using R Proven track record of publishing in international peer-reviewed journals as first author Willingness to conduct fieldwork and participate in