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. Preferred Education and/or Experience: Candidates with extensive research experience in computer programming and data management will be given preference. Candidates with grant-writing experience and a strong
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environments; Proficient in qualitative and mixed-methods data collection and analysis; Demonstrated ability to translate research into usable, operational tools or applications; Strong computer programming and
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laboratory animals, tissue samples, cell cultures, blood samples, or other biological specimens. Demonstrated experience with molecular biology techniques, including recombinant DNA methods and gene-expression
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, plant science, engineering, textile technology, and data science. This team is dedicated to developing and improving methods for assessing cotton fiber quality across production, processing, and end-use
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algorithms to improve the performance of scientific applications Researching digital and post-digital computer architectures for science Developing and advancing extreme-scale scientific data management
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, overlap, and opportunities for integration; explore methods for measuring development and performance; analyze how clinical, laboratory, genomic, environmental, event-based, and other surveillance
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methods; Determining key VOCs that regulate screwworm fly behavior across different physiological conditions; Assist in developing ecological and habitat models to support surveillance and management
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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
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, gaining exposure to public health research, evaluation, and scientific operations that support agency priorities. The participant will strengthen skills in epidemiologic methods, scientific literature
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, including but not limited to deep learning, computer vision, computational linguistics, pretraining methods, interpretability, and transfer learning Experience with cognitive science, particularly