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Field
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training a machine learning algorithm on Greenland satellite – model surface melt differences and characterize what atmospheric forcings are most strongly associated with meltwater production biases using
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transplant recipients and individuals with sickle cell disease, using different tools including multicolor flow cytometry, single cell mRNA transcriptomics (scRNA-seq), and quantitative/functional antibody
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positions in the professional ranks. Preferred Qualifications Strong background in mathematical modeling, optimization, and simulation techniques. Proficiency in relevant software tools (e.g., MATLAB, Python
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high-risk individuals, and tailor treatments based on individual genomic differences. The post-doctoral fellow will work on large-scale genetic data analysis, genetic risk prediction, multi-omics, and
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assurance development and data analysis. A successful candidate will work closely in different aspects of the quality assurance and data engineering pipeline, developing usable and innovative solutions
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modeling (regional planning, ecological, hydrologic climatic, or wildfire modeling). Experience with scientific software development in C/C++, FORTRAN and/or Python. Experience working with geo-spatial
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blot, PAGE, RT-qPCR, RNA immunoprecipitation, etc.) Prior experience working with RNA structure and NGS, as well as experience in bioinformatics and data analysis (i.e. R, Python, Perl) is a significant
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number of different psychopathology and neuroscience research groups. The salary for Post-Doctoral Research Associates in BBS is based on years of experience in accordance with the current NIH (NRSA
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of the following: Ecosystem Modeling, Machine Learning, Microbiome, Microbial Ecology, Soil Science, or Computational Biology. The positions are for several different projects, including the following: (P1
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variety of experimental data, utilize different model structures/modeling techniques, are often closed source or coded in proprietary software packages with poor interoperability, and process experimental