7 postdoctoral-machine-learning "https:" Fellowship positions at Michigan State University
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conduct collaborative research at the intersection of non-canonical kinetic theory, non-conventional plasma turbulence modeling, high-speed flows, scientific machine learning for multi-physics problems
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Type IIP supernovae. We use data from current (ZTF, HST, JWST) and next-generation telescopes/surveys (LSST, WFIRST) to address scientific questions using machine learning and statistical methodology
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Computer programming skills Desired Qualifications Experience developing computational tools for biomolecular systems Experience training machine learning models Experience working in a collaborative
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Experience with multivariate ecological statistics or machine learning and GWA Experience with Field research in agronomy or environmental microbiology Experience with gnotobiotic or controlled
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Experience with multivariate ecological statistics or machine learning and GWA Experience with Field research in agronomy or environmental microbiology Experience with gnotobiotic or controlled
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–functional modeling of root system architecture. Phenomics data integration and high-dimensional trait analysis. Predictive breeding and quantitative genetic modeling. Machine learning approaches to
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big-data lake ecology research using techniques from statistics and computer science (e.g., statistical modeling, machine learning, data mining). Knowledge of open science tools (e.g., Github, Zenodo