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biological mechanisms utilizing U-M Core Facility resources. Experience in Python or R programming is highly preferred. Mission Alignment: Candidates must demonstrate a clear interest in ecology and species
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* Doctoral degree in Kinesiology, Psychology, Computer Science, Biomechanical Engineering, or a related field. A high degree of proficiency using R, Python, and/or Matlab to process and analyze time-series
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coding languages, such as R and Python Willingness to learn new methods and pipeline development by interacting with others Willingness to help train others Ability to read literature and learn vision
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Qualifications* PhD in computational biology, bioinformatics, data science, or a related quantitative field. Proficiency in Python and/or R; experience with high-performance computing environments. Experience with
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epidemiology, policy research, or interdisciplinary public health research is highly desirable. Experience with R, SPSS, Python, Stata, or other statistical programming environments Strong interest in research
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. Applicants should have strong familiarity with data analysis in Python and/or R. Desired Qualifications* Familiarity with Spanish and/or Mandarin will be an added benefit (but is not a requirement). Modes of
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conditions and policy outcomes. Desired areas of expertise include: dynamic and complex systems, agent-based modeling, computer programming (familiarity with R, Python, Netlogo), statistical analysis
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* Experience in one or more of: AAV/vector biology, in vivo gene delivery, mouse models/surgery, single-cell/spatial transcriptomics, ATAC-seq, microscopy, quantitative imaging, computational analysis (R/Python
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researching the auditory midbrain (inferior colliculus). Detail-oriented, quantitative mindset and experience with scientific programming (Matlab, python, or R) is critical. Strong organizational, leadership
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effectively. Strong skills in stakeholder engagement, community-based participatory research or co-design methods. Proficiency with data analysis software (e.g., NVivo, SPSS/STATA/R). Familiarity with