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Geosciences division is hiring an Autonomous Material Processing Postdoctoral Fellow to help transform how critical materials are produced. In this role, you'll bring together machine learning, real-time
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pediatric health services research and child population health research via didactics, structured mentorship, experiential learning through conducting rigorous and ethical research, and professional
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also receive training in use of Excel spreadsheets, PowerPoint, Visio, and plotting and statistical analysis using various software platforms. Learning Objectives: Under the guidance of a mentor you will
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the characterization, safety, biocompatibility, and performance of nanomaterials used in regulated products. Learning Objectives: You will engage in structured research training under the mentorship of FDA scientists
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the various forms of urbanism taught at Taubman College and enthusiastic about the opportunity to teach students in architecture, urban design, urban technology, and/or planning. For more information, visit
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of childhood allergic diseases. The candidate will develop machine learning–based biomarker prediction models to identify microbiome-derived signatures associated with allergy risk and immune tolerance outcomes
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MMA’s program and regional offices, exposing you to a variety of perspectives and issues. Learning Objectives: Under the guidance a mentor, you may gain skills and experience in the following areas: Gain
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. Learning Objectives: By the end of this training/research experience, you will be able to: Explain the structure and functional organization of plant, insect, and/or fungal genomes and describe how genomic
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outside the life sciences (e.g., in physics, chemistry, mathematics, engineering or computer sciences), who have not worked in the life sciences before. Both fellowships provide three years of salary and
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in a Unix environment and on high-performance computing equipment. Learning Objectives: Under the guidance of a mentor, you will have the opportunity to learn to: (a) apply methods in computational