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June 1, 2026, or soon thereafter. The position is within the research section Management and Modelling. The research section Management and Modelling develops methods and tools for herd management
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be used to prepare lamella samples for high resolution cryo-EM imaging and tomography. From AI assisted image analysis, 3D models for key proteins and biomolecular complexes will be fitted into 3D
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of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models. Develop multistate sequence design algorithm for rational design of RNA switches. Develop database
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includes the following tasks: Develop computer-aided design software for modular construction of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models
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or similar. Experience in handling dynamic modelling and control, experimental setup and testing, Digital Twin and Machine Learning Publication experience Collaboration and/or management skills Communication
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research in deep learning models for multi-sensor satellite data (e.g. SAR, SMAP) within a large international research project on AI-driven solutions for groundwater management. Expected start date and
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modelling Experience with hardware-in-the-loop (HIL) emulators such as dSPACE, OPAL-RT, and Speedgoat Experience in power systems and system optimization Knowledge of battery storage technologies and battery
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Mechanics and Turbulence” group and conduct research on data-driven techniques for turbulence modeling in LES and RANS. The initial contract will be for one year, with the possibility of an additional one
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ecological data collection. The positions focus on improving detection and classification performance of deep learning models applied to millions of images collected in European monitoring programs. Key
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identification and population monitoring Contribute to automated approaches for tracking long-term population trends in wildlife Collaborate with colleagues on ongoing modelling and simulation work across the