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. The position is aimed at a method-oriented researcher interested in developing and applying computer vision–based methods in real-world settings. The project is part of the European Partnership on Animal Health
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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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include: Intrinsic uncertainty estimation in MLFFs (epistemic & aleatoric uncertainty) Negative log-likelihood and calibration methods for force-field training Feature-space and orbit-based analysis
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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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analysis, and/or computer modeling. The undergraduate research assistant would work with a graduate student, research professor, and/or professor to complete both lab and field work to support stormwater
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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