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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 29 days ago
-line environment and analyzing whole-genome sequencing data (genome assembly, annotation, and detection of resistance determinants) using at least one scripting language (e.g., Python, R, or Bash
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, bioinformatics, statistics, machine learning, computer science, mathematics, or a related discipline. have demonstrate experience in Python programming and high-performance computing, together with experience in
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with in vivo calcium imaging (either neurons or glia) Experience with microglia biology or neuroimmune interactions Behavioral experiments Solid data analysis skills (e.g., MATLAB or Python for calcium
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years. Demonstrated research experience in cropping systems, evidenced by peer-reviewed scientific publications. Experience in data analysis and modeling using R and/or Python. Knowledge of statistical
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etc.) Preferably knowledge in the characterization of microbial rhodopsins Skills in data analysis with Python Excellent organizational, interpersonal and communication skills The ability to work in an
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ultra low temperature cryogenics (~ mK) Experience with cleanroom work Experience developing a Python-based laboratory control and data-acquisition infrastructure Applications should include: A curriculum
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characterizing materials in the lab, with a background in basic scientific programming (python/web) to build and leverage AI workflows. In addition to experimental work, the postdoc will work with our open-source
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, or control theory Proficiency in MATLAB, Python, or equivalent scientific computing tools Strong writing, verbal, and interpersonal communication skills are required Proven ability to work independently and as
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computational biology, biochemistry, structural biology, bioinformatics, statistics, machine learning, computer science, mathematics, or a related discipline. have demonstrate experience in Python programming and
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biological datasets. Proficiency in R and/or Python and experience working in Unix/Linux computational environments. Familiarity with statistical methods for genomic data analysis, including differential