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, numerical method, and data-driven/machine-learning approaches, including: · Numerical simulation of kinetic equations: fast algorithms with stability/convergence analysis; · Data-driven and machine
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trials to multi-omics explorations, imaging, biomarker development, stable-isotope tracers, and large-scale data set s and machine learning. The lab is now recruiting for a postdoctoral researcher, who
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. Bioinformatics analysis of data will include use of population genomics techniques, phylogenetics and machine-learning development. The successful candidate will be expected to write up results as manuscripts
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physiology, time-series analysis, machine learning and deep learning, multi-modal data integration, systems biology and/or mathematical and mechanistic modeling are beneficial. An ideal candidate will
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Element Method modeling (Abaqus, Ansys) or similar. 4. Basic familiarity with machine learning (preferred). 5. Some record of research output (e.g. peer-reviewed papers, conference abstracts) or
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modeling, medical image processing, and machine learning techniques. Excellent communication and written skills. Required Application Materials CV and cover letter Website me.msu.edu MSU Statement Michigan
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functionalization, materials characterization, various microscopy methods (darkfield, photothermal, ultrafast, interferometric), electron microscopy, machine learning and other advanced statistical methods
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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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outcomes. Methods used in our research group vary widely from randomized clinical trials to multi-omics explorations, imaging, biomarker development, stable-isotope tracers, large-scale data sets + machine
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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