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work on the development and implementation of methods for improving small area modelled human population estimates. The successful candidate is expected to have strong quantitative skills and experience
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Carnegie Mellon University, Institute for Computer-Aided Reasoning in Mathematics Position ID: 3637-PF [#27988] Position Title: Position Type: Postdoctoral Position Location: Pittsburgh
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, alignment, evaluation). Design multi-MLLM collaboration methods (knowledge transfer/distillation, federated learning). Build efficient training/benchmark pipelines and report results with clear metrics. Apply
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. To perform any other duties related to the research program. Job Requirements: Preferably PhD degree in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly
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models, focusing on industrial image analysis Develop advanced deep learning methods for power battery inspection models Design and implement novel algorithms for AI-based CT imaging Lead experimentation
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Fellow to work on the development and implementation of methods for improving small area modelled human population estimates. The successful candidate is expected to have strong quantitative skills and
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of faculty, students and alumni who are shaping the future of AI, Data Science and Computing. Key Responsibilities: Literature review of existing methods and models Identify the weaknesses of existing models
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. To perform any other duties related to the research program. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical
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), (c) estimation methods for latent variable models (e.g., two-step approaches or approximate maximum likelihood estimation), or (d) meta-analytic models to address complex data structures (e.g., spatial
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The Computer Vision-Core Artificial Intelligence Research (Vision-CAIR ) group led by Prof. Mohamed Elhoseiny at the CS Program of the King Abdullah University of Science and Technology (KAUST) is