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-reviewed journals. Attend conferences to disseminate the ideas. Share the Python codes in a well-maintained GitHub repository for public use. Make R or Python package for widespread use of the methods. May
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reports. Job Requirements: PhD in Physics, Applied Physics, Plasma Physics, or related field. Demonstrated experience in scientific computing and numerical modelling (e.g., Python/C/C++/Fortran). Experience
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pipelines in R, Python and HPC or cloud environments. Collaborate with developmental biologists to interpret genomic findings, validate results and address key evolutionary questions. Support and mentor PhD
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. Familiarity with NGS data analysis and command-line bioinformatics (e.g., CRISPResso, Python or R). Experience mentoring students or technicians and coordinating lab operations. Special Instructions
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notebooks, Python or similar, are merits. About the employment The employment is a temporary position of 2 years according to central collective agreement, with the possibility of an extension of up to 1 year
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with scientific programming and quantitative data analysis, particularly in Python, is welcomed. Experience with droplet generation, thermal diagnostics, image processing, or automated experimental
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analyze multi-omic data provided by our collaborative partners and interpret the results from a biologically relevant perspective. Planned activities include Python code development, the analysis and
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, including imaging mass cytometry (IMC) Advanced programming and data analysis skills in R and/or Python Downloading a copy of our Job Description Full details of the role and the skills, knowledge and
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and experience in one or more open-source languages (e.g. Python, R). Prior experience/knowledge of computational psychiatry methods for modelling cognitive tasks. Experience in the use of neuroimaging
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
, Competencies, and Experience Strong programming skills in Python and/or R. Experience analyzing high-dimensional biological data, especially single-cell sequencing data. Strong statistical and quantitative