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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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. Detection of elements in freight wagons from images using AI techniques – Deep Learning. 2. Detection of defects from images using AI techniques – Deep Learning. 3. Validation on a Digital Twin and real
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, computer science, computer engineering or related subject. We will positively consider previous background in computer vision, deep learning and/or color image processing, as well as previous experience in research
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transistors to solve some of society’s most pressing computational challenges. Silicon-based approaches to quantum information processing offer advantages for scaling such as high qubit density, record qubit
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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progression. Main tasks and responsibilities: Image acquisition (advanced microscopy techniques) and image processing Particle image velocimetry (PIV) and traction force microscopy Development of an analysis
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Cellulose. The contract will be within the framework of Severo Ochoa CEX2023-001282-S funded by MICIU/AEI/10.13039/501100011033. Main tasks and responsibilities: Synthesis & Processing Culture and optimize
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(live and fixed), image acquisition using light microscopy, and image processing and analysis Familiar with at least one bioimage analysis tool: Fiji/ImageJ, CellProfiler, napari, ilastik, cellpose
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-stack data analysis systems and integration of the resulting data according to the data space paradigm. Where to apply Website https://sede.uvigo.gal/public/catalog-detail/27660208 Requirements Research
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Thesis/Final Degree Project) related to the subject matter (punctuation: 45) Item 04: Training in microscopic techniques, image analysis, TILIA, GIS or sedDNA (punctuation: 15) Item 05: Personal interview