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for implementation, data collection, and teacher co-design activities. Please Note: This position is grant funded; future employment may be contingent upon future funding. Qualifications Required Education PhD in
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will play a key role in ensuring that these complex data streams are acquired, synchronized, quality-controlled, modeled and interpreted with the highest level of technical and analytical rigor. Main
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours ago
Posting Information Posting Details Department Pathology Lab Med - Research - 413804 Posting Open Date Application Deadline Open Until Filled Yes Position Type Postdoctoral Scholar Position Title
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program for Data-Driven Life Science (DDLS) . The DDLS program focuses on the integration of multi-omics, the interplay between genetics and phenotypes, and the development of AI-driven strategies/tools
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: Statistical programming and data analysis using R, Python, or Matlab, Spatial data analysis or GIS Whole-genome sequencing, phylogenetics, or phylodynamics Data management and reproducible analytical workflows
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to design and execute experiments, analyze data, write, and prepare independent grant applications and manuscripts for scientific publications, and supervise graduate students. Job Description: Key Roles and
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assessments What You’ll Need to Thrive You will be successful in this role if you bring: A PhD in Electrical Engineering, Mechanical Engineering, Data Science or a related discipline Proven experience with
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of packaging designs for high-value product distribution. The ideal candidate will bring strong analytical and experimental skills to address challenges in packaging dynamics, product protection, and sustainable
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their thesis. Profile You hold a PhD in analytical chemistry, environmental chemistry, biomedical or environmental sciences, pharmaceutical sciences or a similar discipline. The preferred candidate should have
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perception at the landscape scale under different scenarios and employ behavioural experiments and Big Data analytics to understand how changes in time perception influence pro-environmental and sustainable