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weak gravitational lensing, galaxy clusters, or large-scale structure. Experience with cosmological simulations and machine learning is highly desirable. Postdoctoral associate in exoplanet atmospheres
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scientific programming using Python and MATLAB, including experience with machine learning and data science libraries such as PyTorch, MONAI, TensorFlow, Scikit-learn, and related tools for 3D medical image
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communication skills. • Track record of conducting original research and publishing in peer-reviewed scientific journals. Preferred Qualifications • Experience in remote sensing or machine learning applications
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for soft materials, with particular emphasis on thermo–visco–hyperelastic behavior, integrating continuum mechanics, scientific machine learning (SciML), and computational physics. The project aims
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-body physics nonequilibrium quantum dynamics, to quantum computation, quantum information, and machine learning. The Institute provides a stimulating environment due to an active in-house workshop
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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, dimensionality reduction and/or machine learning methods (e.g., Lasso, ridge regression) is highly desirable. Familiarity with neurostimulation, Parkinson’s disease, or neuropsychological assessment tools is
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for studying spatiotemporal protein interactions. The postdoctoral fellow will have the opportunity to: - Learn novel research techniques for genome-wide screens - Explore molecular mechanisms of mitochondrial
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will continue to build from our learnings. https://pubs.rsc.org/en/content/articlelanding/2025/gc/d5gc01813g https://pubs.rsc.org/en/content/articlehtml/2018/gc/c7gc03747c https://pubs.rsc.org/en/content
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factors. Though not required, we are particularly interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and