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student will work on data processing from the digital photosensors and image reconstruction for the PET scanner. The student will be involved in the hardware work on the photosensors as well. The same
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physics, materials science, computer science or a related discipline background in one or more of the following areas is desired: X-ray or electron imaging techniques, and image processing proficiency in
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organizational levels of the brain – from molecular and cellular processes to complex neuronal networks and behavior. The Department Cellular Neuroscience of Prof. Dr. Stefan Remy in association with the Research
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coupled neuronal and vascular dynamics of the human retina. Scientific Objective The goal of this PhD project is to develop new optical methods and advanced image-processing approaches to simultaneously
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required): • Plant physiology, root biology or plant–environment interactions. • Experimental approaches to studying internal plant environments or spatially structured biological processes. • Imaging
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available, including high-parameter flow cytometry, confocal imaging, histopathology, metabolomics, single-cell sequencing, and bioinformatics support. Terms & Conditions: €25,000 annual stipend plus academic
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of Systems Biology and Biomedicine - in the lab, in the clinic and in silico. We focus on neurodegenerative processes and are especially interested in Alzheimer's and Parkinson's disease and their contributing
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of rain drops by purely thermodynamic means is not possible in such a short time scale highlighting the importance of turbulence within clouds to these processes. Cloud turbulence causes collision between
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continuum within the 2nd phase of CLICCS (https://www.cliccs.uni-hamburg.de/about-cliccs/cliccs-ll.html). In CLICCS-M4, we are further developing the unique ICON-Coast model within the ICON Earth System
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 20 days ago
Traitement du Signal et des Images, 2023. Douté, S., Forbes, F., Borkowski, S., Meyer, L., Heidmann, S., 2024. Massive analysis of multi-angular images by inverse regression of reflectance models