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Field
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advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities such as CT, MRI, X-ray, and ultrasound. Research areas include image segmentation, detection
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community (cryosphere, geophysics) with good knowledge of image processing and off-the-shelf AI tools, or from the signal/image/AI community with experience in applications related to Earth observation
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process diagnostics with closed-loop process control of AM platforms, and work with fellow researchers to implement machine learning algorithms based on in-situ data collection and analysis. Minimum
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: Catherine Hartley) at New York University seeks a Postdoctoral Associate to work on neuroimaging and behavioral studies examining learning, memory and decision-making across development. NYU offers
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computational imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely connected
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and enthusiastic individual who meets the following criteria: Recently earned a Ph.D. in bioinformatics, computational biology, computer science, electrical and computer engineering, or a related
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multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities
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computer vision methods such as optical flow or motion estimation Experience with geospatial data processing (NetCDF/CF, GeoTIFF, xarray, GDAL/rasterio) Experience with GPU computing or deep learning