Please see below the details and the application form for *paid opportunities for graduate students interested in the analysis of agricultural genomics data sets* at the University of Arizona.
Funding is available to support students interested in analysis of agricultural genomics data sets. We will provide $8,000 per student for graduate students to work on analyzing publicly available sequencing data either over Summer or Fall of 2023. Graduate students need to be currently enrolled in a US graduate program and located at a US institution. In particular we are interested in graduate students who will work with either • short-read Illumina data analysis against a reference genome; OR • long-read data sets generated from PacBio; OR • long-read data sets generated from Oxford Nanopore. Analysis will require the use of open-source software and publicly available data sets from agricultural species. The student’s advisor or mentor should have demonstrated expertise in the required analysis methods and in timely submission of sequence data to public archives. Students will be required to provide comprehensive documentation of their analysis methods using readthedocs or protocol.io and be available in the following semester to answer questions about their workflow and documentation (no more than 3 one-hour online sessions). Applications are due on May 23 and can be submitted via this link: https://uarizona.co1.qualtrics.com/jfe/form/SV_0rh6csLi7mAx3ZY
Applications should include: 1. A statement of up to 250 words briefly outlining experiment design, detailing which samples will be analyzed and what software will be used. Note that the emphasis should be on troubleshooting and optimizing data analysis rather than completing a large number of samples. 2. A statement of up to 300 words outlining evidence of their commitment to documentation and public release of computational workflows (e.g., the research group’s best practice, github contributions, publications). 3. A letter of support from their mentor or advisor briefly describing lab expertise in the analysis method and confirming that that the data set is publicly available. 4. Select if they are requesting support for Summer 2023 or Fall 2023. Successful applicants and their mentors will be required confirm that the student will be available in the semester following their funding on this project to answer questions about their workflow and documentation. Applications will be reviewed based upon the following criteria: 1) Student preparedness for completing the analysis workflow (e.g., understanding of software and analysis optimization). 2) Evidence of the research groups’ commitment to documentation and public release of workflows. 3) The mentor’s history of public data release and well documented analysis pipelines. 4) Feasibility of completing analysis of the samples in the time frame of the project.
Please contact Fiona McCarthy (email@example.com) with any questions.