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extensibility. The ideal candidate for this position will possess: A strong background in scientific programing Interest in developing open-source data analysis and modeling tools in one of the following
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natural sciences Experience with scientific programming and Unix operating systems Experience with various programming languages, including C++, Python and Matlab Ability to work with large datasets
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for decarbonization strategies o Fluency in task automation, data cleaning and analysis in a programming language, with a preference for Python and/or R o Strong organizational skills and exceptional attention
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the opportunity to develop intellectual skills to reach their own, informed conclusions; not require, favor, disfavor or prohibit speech or lawful assembly; create a community dedicated to an ethic of civil and
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programming language (R, Python, or Perl) and experience working in Linux and/or high-performance cluster environments. A strong ability to perform analytical reasoning to extract biological insights from data
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are required to have a Ph.D. or equivalent in biology, ecology or related fields. Excellent writing ability in English is essential as well as creativity, energy, and the desire to work in an interactive and
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Associate in PFAS Predictive Modeling with the University of Maine Cooperative Extension will establish and sustain a dynamic research and educational outreach program to address data and modeling needs
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(Programming Language), R Programming Driver's License: A driver's license is not required for this position. More About This Job Required Qualifications: PhD in Genomics or a related discipline. Familiarity
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publications, excellent written and oral communication skills are required. Preference will be given to those with research experience in hematology (physiology/ pathology), immunotherapy (checkpoint blockade
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Associate or by others in the research group, by collaborators in other groups or from publicly available datasets. The project may also involve use and development of software programs for analysis of multi