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, pneumonia, and tuberculosis. Functional/ mechanistic validation of regulatory elements and gene networks controlling inflammation, immune memory, and tissue repair will be implemented using CRISPR inhibition
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methods with optimization and decision-support models. Background in one or more of the following: time-series analysis, neural networks, forecasting, uncertainty quantification, sensitivity analysis
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both theoretical progress and practical implementation in communication networks. Through these efforts, the Postdoc will generate impactful academic contributions that support the design of next
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will contribute to and lead research in the following areas related to our current and past projects https://gxrlab.rf.gd/research/ : Intelligent Motor Skill Learning: Developing AI-driven, physics
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-free algorithms for real-time optimization of turbine operating conditions (e.g., yaw set points). Other projects may be assigned by the supervisor depending on skills and technical needs. The successful
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. The project will involve drone optimizations, computer vision, closed-loop control, multi-modal sensor fusion, machine learning, and transfer learning methods. The candidate will also work in collaboration with
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. Apply deep learning approaches to support optimization of segmentation methods for clinical neuroimaging datasets. Investigate developmental differences in infant brain functional networks. Support
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, comparative metabolomics, and MS/MS networking. Job Description: A postdoctoral Associate position in metabolomics and natural product chemistry is available as part of an NIH-funded project on nematode
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. 2. Personalized prognostic models of time to loss of independence. 3. Identification of characteristics associated with ATT initiation to optimize referral pathways. 4. Real-world ATT outcomes
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and technologies. Knowledge of AI/ML is important, but the focus will be on the high-fidelity simulation environments, software networking bridges, and physical data collection architectures