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, supported by advanced systems-level modelling and close collaboration with industrial and policy stakeholders. The successful candidate will contribute to the HyperCap research program focused on developing
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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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research project The project will address distinct metabolic pathways in prostate cancer progression. The study is based on an in vivo CRISPR mouse model for prostate cancer, introducing multiple mutations
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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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, Recombinant protein expression and purification, biochemical and biophysical characterization of nucleic acids Computational model building and structure prediction Single-molecule fluorescence microscopy
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quantitative and statistical modelling approaches to biological systems (including crop genetics, host-pathogen interactions, pathogen population genetics, evolutionary biology...). The candidate will work in
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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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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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Electrophysiological characterization of muscle fiber excitability (in collaboration with the research group) In vivo studies using animal models of neuromuscular disease Integration of molecular and transcriptomic data