54 modal-analysis-machine-learning Postdoctoral positions at The Ohio State University
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in life cycle analysis for separation science are required. Desired: Research interest in statistical data analysis is desired. Strong software skills in Excel and Minitab or other statistical
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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researchers to be productive in research and establish academic careers. The Post Doctoral Scholar will work closely with the PI and a doctoral student. The research team will try a mixed method analysis by
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the implementation of machine learning/AI methods for studying complex human diseases, such as glaucoma and retinal diseases. The candidate will have the opportunity to lead research projects, write
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; previous experience in more than one of the following: primary human melanocyte culture, inflammatory skin disease, single cell bioinformatic analysis, murine models; and a proven ability to work both
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Upgrade for the high luminosity running of the LHC and searching for Beyond the Standard Model physics using effective field theory. The Post Doctoral Scholar will take a leading role in analysis of Run 2
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. The research involves the development of practical and computationally efficient methods for adapting and fitting models from survival analysis to infectious disease transmission data and other data, including
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analysis to explore microbial communities and their functional potential. Apply computational tools and pipelines to analyze large-scale biological datasets. Scientific Communication (20%) Contribute
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literature. Essential duties include: to assist and communicate with Principal Investigator (PI) with research lab experiments and sample processing/analysis; assists with evaluation of tests and procedures
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Primary Responsibilities: Lead and contribute to experimental design, data collection, analysis, and manuscript preparation Mentor and supervise undergraduate and graduate research assistants Support the