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Field
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genetics of disease models, human pluripotent stem cells derived neurons, high content imaging, electrophysiology, single cell RNA sequencing, bioinformatics, and spatial transcriptomics technologies. We
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transportation operations and network modelling, accessibility analysis, data analysis (statistics and/or machine learning methods), and spatial mapping. Because the work will involve multiple years of daily
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of the unique genetically modified animal models The Division provides a highly collaborative environment and the possibility to grow. An NIH-funded T32 Training Program is in place. Indianapolis is the capital
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methods for multi-omics data analysis. Potential projects may include developing methods for studying tumor microenvironment using spatial transcriptomics data, proteogenomics integrative analysis
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balance modeling techniques (see below) with existing datasets of field-observed microclimate measurements (e.g. temperature and soil moisture) under various vegetation structural conditions. Objective 2
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for disease prevention and control. ODeSI has extensive experience in operational research and field surveys (including international projects), predictive risk mapping and modelling (spatial epidemiology
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contribute to a comprehensive understanding of gene regulation from DNA to proteins. Key scientific questions will include: How do 3D genome organization and enhancer-promoter spatial associations, as revealed
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lasers (ECDL, Ti:Sa, Fiber), stabilization systems (optical phase lock loop, high-finesse cavity), digital and analog control electronics, active diffractive optical elements (spatial light modulators
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activity in the project, namely within the development of novel numerical simulations and their validation for modeling CO2 storage in heterogeneous reservoirs, capturing mixing across spatial scales
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works to interpret genomes and distill the immensely complex networks that form the foundation of human biology and disease, through accurate machine learning models. Current areas of interest include