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Field
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and interpretation of biomedical data. We have a particularly strong interest in the application of imaging and computing technology to improve the understanding of brain development, to improve
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vacuolar cation channel modulating ion homeostasis and calcium signalling in plants In vivo imaging of calcium and calcium decoding using fluorescence reporters Identification of interaction partners and
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vacuolar cation channel modulating ion homeostasis and calcium signalling in plants In vivo imaging of calcium and calcium decoding using fluorescence reporters Identification of interaction partners and
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an equivalent degree in neuroscience, cognitive science, psychology, or a related discipline-Experience in programming (e.g., MATLAB)-Keen interest in experimental approaches to study predictive processing and
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simulations and their combination with observational data to improve the interpretation of cloud processes Coupling of cloud simulation data with detailed radiation calculation Evaluation of simulated 3D cloud
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) on Foundation Models and/or Deep Learning for Imaging Problems. The Professorship of Machine Learning at the Department of Computer Engineering at TUM has open positions for a doctoral researcher (TV-L E13 100
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-COPD comprises a multidisciplinary team of clinicians, experimentalists, computer scientists and patient representatives working in partnership with a medium-sized company to implement a Personalized
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application domains are e.g., signal-/image processing and machine learning. Tasks: scientific research and development in designing and programming fied programmable gate arrays (FPGAs) for accelerating signal
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techniques, genetics, advanced live cell imaging, and intravital microscopy. Additionally, we will evaluate the clinical relevance of our findings using mouse models and patient samples. Are you a highly
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of Engineering and Design. Our teaching and research focus lies on computer-based development of engineering products, particularly on the planning and realization of built facilities using computational modeling