Hi everybody,

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:

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
4)      Feasibility of completing analysis of the samples in the time frame of the

Please contact Fiona McCarthy (fionamcc@arizona.edu) with any questions.