53 modal-analysis-machine-learning Postdoctoral research jobs at The Ohio State University
Sort by
Refine Your Search
-
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
-
analysis to explore microbial communities and their functional potential. Apply computational tools and pipelines to analyze large-scale biological datasets. Scientific Communication (20%) Contribute
-
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
-
of scale, and how cultural exchanges acquire or do not acquire larger significance. Scholars and creative practitioners working in community engaged contexts whose work directly participates in processes
-
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
-
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
-
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
-
; 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
-
. 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
-
analysis to explore microbial communities and their functional potential. Apply computational tools and pipelines to analyze large-scale biological datasets. Scientific Communication (20%) Contribute