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Post-doc in Machine Learning for Image Processing on Embedded Devices (V24.0155) « Back to the overview Job description We are looking to fill a two-year postdoctoral researcher position as a part
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project will be to develop machine learning models capable of robustly classifying images of agricultural crops based on markers for health state of the crop and deploying those models on a variety of low
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project through DigiAgro3 2024. The main goal of the post-doc project will be to develop machine learning models capable of robustly classifying images of agricultural crops based on markers for health
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medical image analysis, computer vision, or machine learning. You have a clear interest to develop artificial intelligence algorithms and an affinity with healthcare. In addition you possess the profile
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imaging of biomolecular processes in reconstituted systems and in cells. You will collaborate with leading scientists and help drive correlative high-resolution imaging at ultrafast timescales. You will
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of novel drug carriers in combating infections Perform imaging and omics analysis to study microbe-host interactions in vitro Take the lead in data analysis and compile research findings into scientific
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of wildlife to the species level, that will facilitate image processing; develop tools for automated tracking and counting of animals across sequences of images, to inform density-estimation models; develop
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of computational imaging, a marriage between cleverly designed imaging systems and sophisticated post-processing. In everything we do, there is a core of cutting-edge physics and engineering of high-tech instruments
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are pioneers in developing advanced concepts of computational imaging, a marriage between cleverly designed imaging systems and sophisticated post-processing. In everything we do, there is a core of cutting-edge
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Challenge: Ultrafast imaging of biomolecular processes at high resolution. Change: Combining single-molecule fluorescence microscopy and cryo-EM/ET. Impact: Driving a profound understanding of life