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Field
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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computational models for industrial capacity planning, logistics optimization, material flow analysis, and supply chain analysis. Apply artificial intelligence, machine learning, LLMs, and advanced statistical
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science, or a related field; experience with using and building machine learning models, developing and validating computational analysis workflows, and developing circuit models is preferred; excellent
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surveillance and preparedness planning using multiple modeling approaches. The successful candidate will develop and implement statistical and machine-learning models, integrate multi-source ecological datasets
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) Experience in genomics, single-cell data, or machine learning (preferred) Why this is exceptional Build next-generation AI models of the human genome Work with one of the richest longitudinal PD datasets
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highly interdisciplinary, integrating big data analysis, state-of-the-art machine learning models, mathematical modeling, and systems biology to elucidate the mechanisms of drug interactions in complex
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track record in neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches
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neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches). * Demonstrated track record in multiscale
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 29 days ago
independently; and ability to work as part of a tightly-knit team. PREFERRED: Experience with theoretical analysis, using and building machine learning models, and developing circuit models. 3/16/2026
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
methodology, machine learning, and biomedical data science. Our research develops rigorous and interpretable methods for high-dimensional biomedical data, with applications spanning cancer genomics