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external) is reimagining the experimental neuroscience pipeline with big data and AI at its core. A central goal of the project is to build a foundation model of the visual brain—a “digital twin” that
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primary research areas: 1) statistical inference in high-dimensional and large-scale testing scenarios; 2) the development of novel model architectures for large-scale proteomics data; and 3) causal
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motivated Postdoctoral Research Fellow to develop and apply innovative, data-driven models for seismology, with a focus on developing cutting-edge foundation models. This is an exceptional opportunity
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health data e.g., electronic health records, and/or satellite-derived data analysis products and GIS data as well as experience in medical large language models and foundation models e.g., through
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, and contributing to a robust data infrastructure that makes large-scale, multimodal datasets FAIR and AI-ready. You will publish findings in high-impact journals, present at major international
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agenda, we seek applicants with experience in survey design and computational methods, and working with complex large-scale data. Successful candidates will have completed a Ph.D. in a relevant discipline
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leverage the GFDL-MOM6 North West Atlantic model to perform a range of simulations under the shared socioeconomics pathways (SSP) future scenarios. Analyses will involve interpreting and analyzing large data
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of atomic environments Detection of extrapolation and low-reference data regimes Active learning in configurational and chemical space Training and benchmarking of large-scale foundational MLFF models More
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models, assessing their effects on large-scale-structure (LSS) statistics as measured by the power spectrum and bispectrum of galaxies or intensity maps. The project emphasizes spectroscopic galaxy surveys
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of dynamical systems, which will be integrated into large-scale optimization frameworks to enhance the efficiency and reliability of power grid operations. The Postdoctoral Appointee will be responsible