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sponsors. Make decisions concerning project and project spending, within project parameters and guidelines. Facilitate collaboration and best-practice sharing. Regularly review and evaluate opportunities
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sponsors. Make decisions concerning project and project spending, within project parameters and guidelines. Facilitate collaboration and best-practice sharing. Regularly review and evaluate opportunities
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modeling, sensitivity and robustness analysis, Bayesian inference, inverse problems, parameter estimation, or model validation. Experience or strong interest in scientific AI/ML, including surrogate or multi
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calibration, parameter estimation, uncertainty assessment, and decision support. Build and apply hardware-in-the-loop test platforms to validate the full sensing, timing, modeling, and control under real-time
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Gaussian-process emulators for accelerating parameter estimation and uncertainty propagation Selective cross-scale evaluation using complementary ecosystem observations (e.g., experiments) to test how AI
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. Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions. Knowledge of federated learning SOTA algorithms. Knowledge of distributed
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a focus on multimodal learning, computer vision, and scientific machine learning Develop novel algorithms and architectures for tasks such as multimodal retrieval, reasoning over complex data, and
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written and verbal reports to program sponsors. Make decisions concerning project and program spending, within program parameters and guidelines. Facilitate collaboration and best-practice sharing among