We are accepting applications for our USDA-funded Research and Extension Experience for Undergraduates program, "Pigs, Poultry, the Planet, and Data-Driven Problem Solving" (nicknamed "P4"). The P4 program is designed to develop the next generation of data-savvy food animal production professionals, extension associates, and researchers, and we are seeking students with interests in food animal production to apply. As part of the program, students will gain foundational coding skills (no prior coding is necessary or expected!), work on a data-intensive research project, prepare science communication products, and network with professionals working in food animal production. The students will also present at a conference, all expenses paid. P4 students will be paid a $5,000 stipend for the summer. Our program will be virtual this year due to the pandemic. Learn more and apply here: https://p4.rbind.io/
Applications are due this Friday, February 19th, by 5:00 PM! Only undergraduate students who will still be enrolled through summer 2021 are eligible to apply. Students who will have graduated in, or prior to, May 2021 are not eligible to participate. All applicants must be U.S. citizens due to funding restrictions. More details on eligibility are included on the webpage. Applications should include a personal statement, contact information for a reference (note: recommendation letters are not required), resume, and 5-10 sentence response to the question "What is the statistical method you most enjoyed learning about and why?"
Applications are due this Friday, February 19th, by 5:00 PM! Only undergraduate students who will still be enrolled through summer 2021 are eligible to apply. Students who will have graduated in, or prior to, May 2021 are not eligible to participate. All applicants must be U.S. citizens due to funding restrictions. More details on eligibility are included on the webpage. Applications should include a personal statement, contact information for a reference (note: recommendation letters are not required), resume, and 5-10 sentence response to the question "What is the statistical method you most enjoyed learning about and why?"
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