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Remote Graduate Research Assistant

Dr. Alan Cleary is actively seeking a graduate student to work on the Pangenomic Data Compression Project. The goal of the project is to develop new algorithms and data-structures for the compression of pangenomic DNA sequence data that allow analyses to be performed directly on the compressed archives. Analyses of interest include sequence search, read mapping and alignment, and operation algorithms that allow compressed archives to be updated over time.

The position is remote since the student must be enrolled as a full-time graduate student at their university to qualify. Compensation will be commensurate to what Research Assistants are paid at the student's university and include tuition but not fees. The start date of the position will coincide with the Spring semester of 2022 at the student's university and may continue into the summer and following semesters.

This posting will remain active until the position is filled. Please contact Dr. Cleary directly with any questions or to apply: acleary@ncgr.org. If applying, please include a description of why you're interested in working on the Pangenomic Data Compression Project.

Education


The student must be a full-time graduate student at a university in the United States.
The student's degree program must be in Computer Science or a closely related field.

Qualifications


The ideal student will have experience in discrete algorithms, especially with respect to modern string algorithms and data structures (BWT, wavelet trees, FM-index, LCP intervals, context-free grammars, etc). We intend to produce scalable, production grade software so strong programming skills are also desired, though not required.

About NCGR

The National Center for Genome Resources is a not-for-profit research institute that innovates, collaborates, and educates in the field of genomic data science. As leaders in DNA sequence analysis, we partner with government, industry, and academia to drive biological discovery in all kingdoms of life. We deliver value through expertise in experimental design, software, computation, data integration and training a skilled workforce.

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