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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 21 hours ago
Rohrbach and T Tuytelaars Code: https://github.com/rahafaljundi/MAS-Memory-Aware-Synapses Theme C Vision&Language learning (selected papers): VisualGPT: Data-efficient Image Captioning by Balancing Visual
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reproducible code by using code sharing platforms, version control, workflow languages and container solutions is a merit. Experience in training deep learning-based models on HPC-resources is also meriting. A
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reconstruction techniques; experience with multi-nuclear image reconstruction, Siemens MR IDEA Image Calculation Environment (ICE), Python and MATLAB, and deep learning techniques are considered assets A
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Grafing bei München, Bayern | Germany | about 2 months ago
machine learning for imaging, ideally in biomedical or histopathological contexts Strong programming skills and deep learning frameworks Familiarity with generative models (e.g., diffusion, GANs
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, migration, or advanced geophysical interpretation. Ground Penetrating Radar (GPR) applications and signal processing. Image and signal processing for geophysical datasets. Machine learning or deep
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tracing, CRISPR screens or other high-throughput perturbation approaches. Experience with quantitative image analysis and deep learning tools for segmentation and classification (e.g., Cellpose
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in magnet configurations, RF/gradient coil engineering, pulse sequence programming, and shielding-free system architectures. (2) Computational Imaging & AI: Deep learning and data-driven image
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microfluidics, nano-electronics, nano-biomaterials, big data, and deep learning. Applicants must hold an M.D., Ph.D., or equivalent degree and have extensive postdoctoral experience, along with a strong
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[IMAGE] Flatiron Research Fellow, Center for Computational Mathematics Description Applications are invited for postdoctoral Flatiron Research Fellowships (FRF) at the Center for Computational
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tracking using deep learning methods has been shown to deliver excellent results on selected datasets, experimentalists still struggle to track cells automatically on new datasets: the diversity of