41 software-defined-networks Postdoctoral positions at Princeton University in United States
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, strive for excellence, and support continuous quality efforts. As a member of the UHS staff, your job may be deemed essential as defined by University and UHS policy. Please ask you supervisor
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opportunity to work closely with our engineers and other research staff. Projects may include software package creation and maintenance, data engineering, development and/or implementation of advanced
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models, programming, and quantitative methods. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel
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expects to have post-doctoral or more senior research positions in Theoretical High-Energy Physics, broadly defined, starting around September 1, 2025. The applicants should have a Ph.D. in Physics and
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include: *Doctoral degree in economics or political science. *Academic experience in economic theory, game theory, and statistical analysis *Proficiency with relevant software, e.g. Python, R, and STATA
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the Department of Chemical and Biological Engineering to study the biochemical and mechanical mechanisms that define pattern formation during branching morphogenesis of the lung and mammary gland. Further
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methodologies for modeling and analyzing spatially embedded networks. This project aims to advance the understanding of infrastructure systems by leveraging spatial networks to capture complex interdependencies
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of epistemic values in scientific practice, or the expression of values in collective behaviors (e.g., in online social networks). The proposed research is expected to yield both theoretical and empirical
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, multiscale modeling, molecular simulation code/software (e.g., LAMMPS, GROMACS), machine learning. Prior experience with applying simulations to biomolecular systems is a plus but not required. Applicants
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation