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professional goals. Along the way, you will engage in activities and research in several domains. Available topical areas include, but are not limited to: Optimization Reinforcement learning Bayesian analysis
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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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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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. 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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into their professional development. Learning Objectives: This research fellow will gain experience in bioinformatics and computational biology, with the focus on arthropod genomics and genetics. The fellow will learn
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also includes screening individual phytochemicals and extracts from additional plant materials to determine their anticancer and antioxidant activities. Learning Objectives: The participant will receive
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resources to support development of rich datasets for asking complex questions and collaborate broadly across many different research communities. Learning Objectives: The participant will learn techniques
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resources to support development of rich datasets for asking complex questions and collaborate broadly across many different research communities. Learning Objectives: The participant will learn techniques
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or viability of encapsulated bio-components. Why should I apply? This appointment offers training and travel opportunities to enhance your fellowship experience. Anticipated learning objectives include