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Field
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candidate will design and develop digital twin algorithms for computational fluid dynamics, contributing to innovative solutions in healthcare software systems. This role involves collaboration with research
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machine learning algorithms for various research projects creating medical image automation algorithms writing combat casualty care relevant military research proposals preparing manuscripts for submission
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Science and Engineering, or a related area is required. The position will involve developing models and algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research
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Engineering, Aerospace Engineering, or a related field. Degree must be conferred upon hire. Preferred Qualifications Applied expertise in optimal control, heuristic optimization, graph search algorithms, and
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algorithms, and machine learning to solve complex aerospace engineering challenges. Developed and implemented AI-driven solutions for autonomous lunar and asteroid landings, as well as cislunar operations
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reconstruction algorithms that incorporate multiply-beam coherent scattering imaging in a grazing incidence geometry to improve the spatial resolution to ultimately demonstrate the utility of the novel coherent
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on using unstructured and overset meshes with high-fidelity algorithms to obtain scale-resolved data. Candidate will also post-process data using data-driven and physics-driven methods to extract fundamental
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forecast modeling and scenario development for climate change and water allocation. The successful candidate will leverage existing water quality algorithms and products (e.g., temperature, turbidity
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/or observationally motivated model-building for SMBH feeding and feedback. The group integrates observations, theory, data analysis, algorithm development, model-building, and numerical simulations
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Requisition Id 15253 Overview: We are seeking a Postdoctoral Research Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted