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at the forefront of nano-optics and nanophotonics within the Vectorial Nano-Imaging research unit at Istituto Italiano di Tecnologia, led by Dr. Antonio Ambrosio. The research activity focuses on the investigation
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soft matter, with expertise in microscopy, optical imaging, and data analysis. Experience in building and working with optical traps will be highly rated in the selection process. The project also
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: Experience in ultrafast single shot imaging and ultrafast mapping photography. Experience with advanced optics for beam wavefront manipulation including adaptive optics and orbital angular momentum beams
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unprecedented precision, one at a time and in real time (Nature, 2010; Nature Nanotechnology, 2012, 2017; Science 2025). The project involves a combination of optical imaging, nanofabrication, data analysis, and
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areas:Miniaturized optically pumped atomic magnetometers (OPMs) for ultra-low-field nuclear magnetic resonance (ZULF NMR) and magnetic resonance imaging (MRI); NMR MRI with a focus on MRI of microfluidic
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of food ingredients. The research is focused on structural and physical aspects of food ingredients and food matrices using high resolution microscopy and image analysis. We are seeking a highly motivated
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. Indeed, the methods currently used rely on optical image databases of various avalanche observations. A deep neural network was trained on this data to enable automatic avalanche detection FIGURE 1 (a) [1
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Max Planck Institute for Biological Cybernetics, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 6 days ago
opportunity to apply novel genetic tools to study molecular brain function using optical, ultrasound and magnetic resonance imaging. Our interdisciplinary approach encourages researchers with different
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of Caltech Optical Observatories OIR projects in the division of Physics Math and Astronomy (PMA) through a combination of daily tasks and engineering technical assistance in labs. Essential Job Duties Provide
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will