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that are the fundamental constituents of matter and their interactions. Obtaining scientific results from these experiments requires complex software and computing systems, developed by international teams of researchers
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systems at Princeton and in NOAA, working alongside GFDL model developers and software engineers to advance quality assurance and data dissemination capabilities for making high-resolution earth system
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of the CAD software Rhinoceros 3D, its plugin Grasshopper 3D, and its API RhinoCommon Real-world architecture and/or construction experience What We Offer We offer a competitive package (salary, health
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to work independently and as part of a team in a research environment. Experience with astronomical inference and the scientific Python software ecosystem is preferred. The successful candidate should be
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may include software package creation and maintenance, data engineering, development and/or implementation of advanced statistical methodologies, and supporting research on high performance and cloud
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technologies, computer vision, and perception Foundational knowledge of machine learning Experience developing custom tools and end effectors for robotic assembly Good knowledge of the CAD software Rhinoceros 3D
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team in a research environment. Experience with astronomical inference and the scientific Python software ecosystem is preferred. The successful candidate should be willing to engage in interdisciplinary
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, and statistical analysis *Proficiency with relevant software, e.g. Python, R, and STATA. *Excellent writing and analytical skills *Evidence of ability to take the initiative in solving practical
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Artificial Intelligence (AI)/Machine Learning tools, OR a PhD in AI/Machine Learning with experience in plasma physics or other strongly coupled physical systems. Associated research and software engineering