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track record in wireless communication systems, machine learning, or related fields. Ability to work independently and co-supervise PhD students. Excellent command of the English language, both written
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appointment. See the following table(s) for the current salary scale(s) for this position: https://www.ucop.edu/academic-personnel-programs/_files/2024-25/oct-2024-scales/t23.pdf . The current minimum salary
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experience in NGS data analysis and genomics pipelines since similar to proteomic pipelines. Record of prior first-author publications. A deep interest in biomarker research and discovery. The drive to pursue
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expertise in research and development in the following areas of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in
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the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the … Where to apply Website https://www.academictransfer.com/en/jobs/356464/postdoc-deep-learning-based
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effectively in both oral and written forms, including presenting research findings to diverse audiences and contributing to collaborative discussions expertise in deep learning techniques with experience
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should possess a PhD in Computer Science or related area. We are looking for candidates who have publications in top conferences like NeurIPS, ICML, ICLR, CVPR, etc. A strong background in deep learning
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) a PhD in a quantitative discipline such as computer science, mathematics, statistics, engineering, or a related field. Strong programming skills and experience in machine learning or statistical
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 12 hours ago
) asks all programs. Apply now to join the ARPA-E team and shape the future of energy. ARPA-E recruits Summer Scholars who have a unique combination of technical and business skills to learn how
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of machine learning for healthcare and related topics Deep knowledge of multi-modal learning, transfer learning, foundation models, and self-supervised learning. Experience in dealing with large