Quantitative Genetics /Computational Biology Internship

The Role

We are looking for a summer intern currently enrolled in an M.S. or Ph.D. program with strong experience in computational biology, quantitative or population genetics with specific applications to transcriptome data to join our team focused on identifying editing targets to improve complex traits in crops. The internship will last for three months.

As an Intern, you will...

• Work with large scale transcriptome, phenotypic, and genotypic data to discover novel genotype-to-phenotype associations
• Gain familiarity with performing computational biology analyses on modern cloud infrastructure (i.e. AWS)
• Work with Inari's computational biologists to apply tools from other disciplines to improve editing target identification in plant genomes
• Use the scientific literature inform new analyses and hypotheses
• Present your results to stakeholders within the company
• Become familiar with the norms of working in a private sector biotechnology company

You bring...

• Competency with one or more of the following (Python or R) and a Linux/UNIX computing environment
• An understanding of linear mixed models with demonstrated applications to marker-trait and gene-trait association (GWAS, PheWAS, TWAS, QTL mapping, variance decomposition, etc) and/or polygenic risk score prediction / genomic prediction
• Experience with analyzing transcriptome data including, but not limited to mapping, quantification, post-processing and quality control
• Familiarity with analyzing large-scale genetic variant datasets using public tools
• Ability to work in a team in a fast-paced, cross-functional environment
• Ability to manage ambiguity and to exchange constructive feedback
• Adaptability and enthusiasm for new challenges, innate curiosity, and a passion for learning
• Curiosity and a desire to continuously learn and have a meaningful impact
• Creative and strategic thinking, good problem-solving skills, willingness to be bold and take risks, and the ability to recognize and learn from failures

Bonus qualifications...

• Previous experience working with plant genomic data
• Familiarity with the integration of large datasets (metabolomics, proteomics, etc)
• Familiarity with plant or animal breeding methodologies
• Experience working with any of the following: AWS, Google Cloud, Docker, Git, Agile methodologies