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data science techniques to model and classify population health risk and disease dynamics at multiple spatial and temporal scales. The PDRA will lead the development of novel approaches for managing
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. Evaluates and analyzes data. Establishes new research protocols and procedures. Summarizes research findings and publishes results in research journals. Contributes to organizing laboratory operations
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modeling approaches. • Investigate interactions between food products and paper-based packaging materials across diverse product categories. • Collect, process, and analyze experimental data, including
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 2 months ago
-on research experience in the preparation and deployment of the EXCITE payload, with topic areas including: infrared detectors and readout, cryogenics and cryocoolers, optics, data acquisition, balloon attitude
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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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The successful candidate will contribute to the following research axes: AI-driven territorial diagnostics and foresight, integrating multi-source satellite data with machine learning and spatial modeling Climate
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Responsibilities Drive research activities towards defined goals and priorities within a specified timeline Perform advanced level of data analysis (quantitative and qualitative) Advance knowledge based on the data
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-$121,465.00. Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and
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comparable knowledge is demonstrated may be considered. Additional Information: Sponsorship: Applicants for this position must be currently legally authorized to work for CMU in the United States. CMU will not
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-focused , contributing primarily to one of the following projects: Project A – Synthetic Data for Theory-Driven Behavioral Research This project investigates how large language models (LLMs) produce