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like TensorFlow or PyTorch. Strong publication record in top-tier, peer-reviewed journals and conferences. Experience with deep learning applications in the context of wireless communication systems
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Research Center for Molecular Medicine (CeMM), ÖAW | Vienna, Virginia | United States | 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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Experience with deep learning frameworks such as PyTorch or TensorFlow Exposure to AI-enabled scientific workflows that couple simulation with data-driven modeling, including emerging approaches involving
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integrating human-in-the-loop reinforcement learning approaches. Responsible AI: Exploration of privacy-preserving AI techniques, enhancing AI safety, and addressing vulnerabilities and defenses in Large
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental
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studies. Strong background in AI/ML fundamentals and extensive experience with deep learning (DL) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit
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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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applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA
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) mathematics, physics, or a related STEM fields. Strong programming and data analysis skills (e.g., Python, R) Solid understanding of machine learning, deep learning, and data modeling techniques Job Description
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experience with deep learning (DL) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence