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master degree degree is required in relevant areas such as remote sensing, computer sciences, and mathematics. You are also required to have strong background in deep learning for image analysis, e.g
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Project description The postdoctoral fellow will explore synaptic processes and white matter pathways between
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stereolithographic, 3D printing and textile techniques like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro
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stereolithographic, 3D printing and textile techniques like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro
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Protein Expertise Platform, X-ray, proteomics, NMR (850-400 MHz), cryo-EM and Biochemical Imaging Centre (confocal, SIM, FLIM, spinning disk, TIRF, STORM). Project description Autophagy is an evolutionarily
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research involving sampling and detailed characterization of lime and cement clinker products from electrified manufacturing processes. The position is full-time for two years, starting on November 1, 2025
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sampling and detailed characterization of lime and cement clinker products from electrified manufacturing processes. The position is full-time for two years, starting on November 1, 2025 or as agreed
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for multimodal machine learning, combining large-scale image data with molecular profiling and clinical data. This includes, for instance, research on deep learning-based image analysis and data assimilation
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dynamics and heat transfer research. Programming skills in Python and MATLAB, particularly in machine learning, data analysis, and image processing. Experience working in Linux environments. Ability
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machine learning approaches Implement and manage workflows for large-scale data processing on high-performance computing systems Design and perform validation experiments through tissue imaging, molecular