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simulation of scenarios with different materials and geometries. - Support the development and implementation of signal and image processing algorithms, including fast inversion techniques, FFT, and nonlinear
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or equivalent Specific Requirements Item 01: Academic diplomas (degree and master), related to data & image processing (punctuation: 25) Item 02: Enrolled in a PhD program related to Applied Geotechnologies
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techniques and imaging technologies, the unit delivers cutting-edge solutions with real-world impact. Led by Prof. María Cuartero (ERC Fellow) and Prof. Gaston Crespo, UCAM-SENS fosters innovation, education
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tumor-on-a-chip models fabrication and set-up Experience in confocal microscopy and imaging Excellent organizational and multitasking abilities Strong time-management skills Advantageous: Knowledge in
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. BioNano and Medical Photonics Use of light-based techniques, including super-resolution imaging tools, to study processes of relevance in molecular and cellular biology, and in clinical medical processes
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environment Excellent communication skills Desired: Experience and knowledge of histological laboratory techniques Experience with specialised software for image and data analysis Scientific Contributions
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markers, control Marfan and OKD48 mouse colonies; surgery for extraction of various organs. The candidate will join the lab of Gustavo Egea (Vascular Cell Biology Lab), which is currently comprised of 1
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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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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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applications will be probed in order to validate the implementation of these techniques in key computer vision-related tasks, such as image classification, multimodal data generation, or manipulation of graph