107 software-engineering-model-driven-engineering-phd-position Fellowship positions at Zintellect
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) Computer, Information, and Data Sciences (17 ) Earth and Geosciences (21 ) Engineering (29 ) Environmental and Marine Sciences (14 ) Life Health and Medical Sciences (51 ) Mathematics and
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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
. This fellowship aims to improve crop protection from pests through the use of natural products, either by discovering safer natural pesticides or by manipulating their production in plants by genetic engineering or
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/abstract and full-text screening using established software platforms, and extract relevant clinical data from selected studies. Assessing the risk of bias and methodological quality of included studies
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, and marker development. Experiences to analyze high through put data sets including short- and long-read genomic and transcriptomic data, and develop or apply related software for data mining. Familiar
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analytical tools, including software and open-source resources, to evaluate, analyze, interpret, and communicate scientific information relevant to environmental and public health. Training will include
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pursuing a doctoral degree in one of the relevant fields (environmental health sciences, environmental epidemiology, public health, data science and/or environmental engineering). Degree must have been
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through scientific excellence. Research Project: You will engage in innovative research on symbiont technology—a cutting-edge approach that uses Agrobacterium to deliver antimicrobial peptides (AMPs
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) Other Non-Science & Engineering (1 ) Social and Behavioral Sciences (1 ) Affirmation I certify that I have not previously been employed by CDC or by a contractor working directly for CDC. I
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, food science/technology, or a closely related field). Degree must have been received within the past five years or anticipated to be received by 12/31/2026. Preferred Skills: Knowledge of molecular
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machine learning methods, including cluster analysis and predictive modeling, to identify distinct phenotypes of diabetes and characterize factors associated with disease onset, progression, complications