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Field
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sciences.Tackling key problems in biology will require scientists trained in areas such as chemistry, physics, applied mathematics, computer science, and engineering. Proposals that include deep or machine learning
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analysis packages, basic shell scripting, experience in Unix/Linux platform) and experiences with deep learning tools (e.g., PyTorch, TensorFLow, Keras), neuroimaging analysis tools (e.g., PMOD, SPM, FSL
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(such as postdoctoral positions) may be valuable but not required. A thorough understanding of recent trends and developments in the field is essential. A passion for science and a thirst to learn more. You
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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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computer vision tools (e.g., MediaPipe, OpenPose, homography estimation, optical flow). Experience with eye-tracking data collection or analysis. Familiarity with deep learning frameworks (PyTorch
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Research Center for Molecular Medicine (CeMM), ÖAW | Vienna, Virginia | United States | about 2 months ago
on LazySlide ( et al Nature Methods ), our scalable software foundation, and our deep learning framework for age prediction (Abila et al., Nature Medicine, in press) to engineer a body-scale machine learning
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PyTorch or TensorFlow. Experience developing, training, and optimizing neural network and deep learning architectures. Experience with Linux/UNIX environments and HPC systems. Familiarity with job
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related to creating or testing deep learning models for genomics, exploring new techniques related to spatial simulations, or other topics discussed with the PI. Basic Qualifications Core job duties include
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and other postdoctoral researchers as part of our Lundbeck Professorship grant, which you can learn more about here: https://www.cnap.hst.aau.dk/lundbeck-professorship As a postdoctoral researcher your
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Pediatric Bioengineering(HSJD) Specific focus / expertise: Artificial intelligence and data science applied to bioengineering, including (but not limited to): – Machine learning and deep learning for complex