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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
Experience: - Experience in machine learning, network analysis, or statistical modeling. - Familiarity with metabolomics or systems biology. - Experience with cloud computing platforms and/or high-performance
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This project concerns the evolution of the icy grains of the dense
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., TensorFlow, PyTorch). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications: Experience with multi-GPU model training and large-scale inference. Familiarity
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, information processing, computing, cybersecurity, and communications technologies. ISI’s 400 faculty, professional staff and graduate students carry out extraordinary information sciences research at three
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, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong research skills, evidenced by high-quality publications in top-tier machine learning/AI
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, videos, and point clouds. Additionally, candidates will integrate immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), to enable real-time visualization
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. Additional Information Benefits full-time position, annual leave 36 days well equipped facilities, the tools required for research: computer farms and cloud computing infrastructure, software, servers, etc
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: • Experience with next-generation sequencing, spatial transcriptomics, or imaging techniques. • Proficiency in computational biology tools for transcriptomic and genomic data analysis. • Familiarity with animal
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, scientific software development and high-performance computing. The division is currently in an expansive phase in new emerging areas such as cloud and fog computing, data science, and artificial intelligence
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developing and implementing very large deep learning models. Familiarity with high performance computing environments (e.g., HPC clusters, GPUs, Cloud resources) and managing Linux based hardware systems