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Applied statistics Network routing Agent-based simulation Behavioral economics Game theory Decision theory Machine learning Artificial intelligence Where will I be located? Both local and remote
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the use of workflow tools, development environments, and resources to contribute to and implement shared bioinformatic workflows. Experiences may extend into training on Machine Learning and AI models as
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learn and gain experience in: Participating in various aspects of pre-clinical research through ongoing collaborative research projects Applying data collection methods appropriate to existing research
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Learning about protein design and engineering Exploring cell-based and cell-free screening Applying high-throughput screening Utilizing bioinformatics, machine learning, and other computational approaches
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. This data was collected primarily in upland habitats, but a subset of the data focuses on changes in meadows over time. Learning Objectives: The selected fellow(s) in this project will have the opportunity
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analysis of laboratory assay readouts, or processing and analyzing transcriptomics data (bulk or single-cell RNA-seq). Learning Objectives: Under the guidance of a mentor, the participant will have the
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institution systems may be submitted. Click here for detailed information about acceptable transcripts. A current resume/CV, including academic history, employment history, relevant experiences, and
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institution systems may be submitted. Click here for detailed information about acceptable transcripts. A current resume/CV, including academic history, employment history, relevant experiences, and
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ORISE. For more information, visit the ORISE Research Participation Program at the U.S. Department of Defense . Qualifications The qualified candidate will have completed a Bachelor's, Master's, or PhD
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institution systems may be submitted. Click here for detailed information about acceptable transcripts. A current resume/CV, including academic history, employment history, relevant experiences, and