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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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-Energy Physics, broadly defined, starting around September 1, 2025. The applicants should have a Ph.D. in Physics and are expected to demonstrate a strong record of research accomplishment and creativity
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. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel Daw, ndaw@princeton.edu. Review of applications
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boundary layer and apply them to ocean climate models. Our previous work demonstrated that neural networks can learn to predict the vertical structure of vertical diffusivity and the networks can then be
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values in collective behaviors (e.g., in online social networks). The proposed research is expected to yield both theoretical and empirical publications. The candidate will be appointed in the Department
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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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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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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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, 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