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Field
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analysis technology3D high-throughput imaging or patient biosample processing Ability to work in a multidisciplinary and international team of scientists with excellent written and oral communication skills
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cells (e.g., immune cells and tumor cells) in OoC systems Performing imaging experiments using light and fluorescence microscopy Quantitative image analysis and evaluation of immunological responses
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physics, engineering, medicine, biology or a related field Practical experience in optics, signal processing and working with experimental setups (e.g. MATLAB) Interest in medical diagnostics and innovation
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years of experience with extensive expertise in microelectronics and microsystems technology. Our wide range of technologies spans from advanced semiconductor processes and MEMS technologies to innovative
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Course location Hamburg Description/content The Cluster of Excellence "CUI: Advanced Imaging of Matter", which is funded by the German federal and state governments, combines projects in physics
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) department develops innovative deep learning technologies in the area of image and video analysis. The department's competencies cover the entire processing chain, from the collection and analysis of visual
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are shaping the technologies and processes for the future of digital production, packaging and scanning technology and many other laser-based applications, e.g. 3D printing and laser fusion. Are you looking
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Interaction Causal Models and Inference Time Series Modelling Multimodal Data Integration and Modelling Image Recognition and Computer Vision Computational and Simulation Science Visualisation High-Performance
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | about 2 months ago
-class space mission NewAthena, MPE leads the international consortium developing the Wide Field Imager (WFI) , one of the two instruments aboard the X-ray satellite. Further details about the WFI, which
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. Among these, concept-based and prototype-based methods show promise in offering intuitive insights into model decisions. To truly build trust and ensure safe deployment of models, however, it is not