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sociology. Strong quantitative skills and experience with large-scale data analysis required. Computer Science/HCI: PhD in Computer Science, Human-Computer Interaction, Information Science, or related
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machine learning, computer vision, and medical image analysis, with publications in top-tier AI and medical image analysis conferences and journals, including CVPR, ICCV, ECCV, NeurIPS, MICCAI, TPAMI, TIP
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for the university and funding agencies. Job Requirements: PhD qualification degree in Computer, Electrical or Electronic Engineering or related field At least 3 years of relevant research experience in AI security
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, United States of America [map ] Subject Areas: Electrical and Computer Engineering / artificial intelligence , Artificial Intelligence and Machine Learning (AI/ML) Starting Date: 2026/01/01 Salary Range: $62,232-$80,000
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position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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related field Strong background in data analysis, particularly with behavior data, functional ultrasound (fUS) or other neuroimaging modalities Proficiency in statistical analysis, machine learning, and
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about the Shih Lab: Learn more about the innovative work led by Dr. William Shih here: https://www.shih.hms.harvard.edu/ . What you’ll do: Develop DNA-based sensors that seed crisscross assembly of single
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work environment Learn more about CQT at https://www.cqt.sg/ Job Description The CQT S14 team is looking for candidates with strong background in Software Engineering, Computational Physics or Computer
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sheet evolution, methane hydrate fluxes, or applying machine learning to geosciences to reconstruct glacial histories and project future ice sheet behavior. Please read this interview for more details
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches