148 parallel-computing-numerical-methods Postdoctoral positions at Princeton University
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behavioral paradigms and combined with computational approaches. We are seeking an extremely motivated postdoctoral researcher with background in human or monkey electrophysiology. Studies will include
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, combines advanced system neuroscience and computational modeling techniques to study planning in rodents engaged in dynamic spatial foraging tasks. The successful candidate will develop computational models
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of interest to its faculty. Domains of interest include nonlinear partial differential equations, computational fluid dynamics, material science, dynamical systems, numerical analysis, stochastic analysis
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. Some experience with first-principle methods (FP/DFT) and/or other forms of electronic and magnetic structure theory and calculations is also expected. The successful candidate will have a strong
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to ion beams with well-controlled energies and incident angles for benchmarking and validation of theoretical calculations and computational physics and chemistry modeling of important surface processes
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/or energy *Strong methodological and quantitative skills, such as survey and sampling design and data analysis (in R or Python), meta-analysis and/or document/text analysis, or computational modeling
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spectroscopic and imaging techniques for UHV surface science experiments and methods. Additional expertise in plasma, plasma-materials interactions, and/or ALE is of significant value. Experience with the design
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Improving Ocean Surface Boundary Layer Mixing Parameterizations with Langmuir Turbulence and Machine Learning The Atmospheric and Oceanic Sciences Program at Princeton University, in association
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-to-Decadal Variability & Predictability Division, Technical Services and Modeling Systems Division. The selected candidate will have access to state-of-the-art numerical models and high-performance computing
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. The successful candidate must have substantial experience in state-of-the-art ARPES and/or low temperature STM/STS techniques. Some experience with first-principle methods (FP/DFT) and/or other forms of electronic