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Field
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, using R or Python; experience with these datasets is helpful but not required Preparing statistical reports and presentations Presenting findings at regional or national scientific meetings Mentoring
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University of Southern California (USC) | Los Angeles, California | United States | about 19 hours ago
responsibilities for this role include: Initiating, planning, and overseeing research projects Leading the analysis of complex multiomics data, including single-cell and spatial omics, using R or Python; experience
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Marrakech. About CRSA: The Center for Remote Sensing Applications (CRSA) is an interdisciplinary research center within UM6P that spans multiple academic programs. CRSA focuses on addressing food and water
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the use of bioinformatics tools for metagenomic and transcriptomic data analysis (e.g., QIIME, DADA2, R, Python). Demonstrated ability to independently design and conduct experiments, analyze data, and
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clinical translation. Our work is supported by multiple NIH grants, including funding from NIAMS, the NIH AMP-AIM Team Science Leadership Scholars Program, and the Office of Research on Women’s Health, as
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analysis (e.g., R, Python, GraphPad) Research and Analytical Abilities: Experimental design and troubleshooting Complex problem solving and critical thinking Data interpretation and utilization Knowledge
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intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet
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, Mathematics, Computer Science, or a closely related field. Demonstrated expertise working with multivariate time-series and spatiotemporal datasets. Advanced proficiency in Python. 1–2 years of hands
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, based near Marrakech. About CRSA: The Center for Remote Sensing Applications (CRSA) is an interdisciplinary research center within UM6P that spans multiple academic programs. CRSA focuses on addressing
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles