Biostatistics Intern: Accelerate Crop Genetic Gain

Vylor, Inc.

Des Moines (IA)

On-site

USD 20,000 - 31,000

Full time

9 days ago
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Job summary

Vylor, Inc. is hosting a 6-month internship in the Seed Product Development Biostatistics team in Johnston, Iowa. You will work on state-of-the-art statistical methods to accelerate genetic gain in breeding programs and collaborate with cross-disciplinary teams across geographies.

The role targets an advanced student with strong math/statistics background and experience with R/ASReml, willing to relocate and engage in a hands-on project that can lead to full-time opportunities.

Qualifications

  • Must be enrolled at an accredited university during internship.
  • Willingness to relocate to Johnston, Iowa for duration of internship.
  • Advanced graduate studies in statistics or plant sciences are expected.
  • Strong interest in applying statistical methods to commercial agriculture.
  • Solid foundation in math and statistics (linear mixed models, spatial statistics, ordinal data).
  • Experience with large datasets and scientific computing languages (R/ASReml).
  • Excellent oral and written communication across diverse backgrounds.
  • Creative, independent, and collaborative work style.

Responsibilities

  • Develop and deploy experimental designs and phenotypic analysis for genetic gain in breeding programs.
  • Collaborate with plant breeders across crops/geographies to craft statistical solutions.
  • Manage own research project with progress reported to intern group.

Skills

Statistical methods
Linear mixed models
Spatial statistics
Ordinal data analysis
R/ASReml
Data manipulation
Communication skills
Teamwork

Education

Graduate student in statistics or plant sciences

Tools

R
ASReml

Job description

Vylor, Inc. is hosting a 6-month internship in the Seed Product Development Biostatistics team in Johnston, Iowa. You will work on state-of-the-art statistical methods to accelerate genetic gain in breeding programs and collaborate with cross-disciplinary teams across geographies.

The role targets an advanced student with strong math/statistics background and experience with R/ASReml, willing to relocate and engage in a hands-on project that can lead to full-time opportunities.

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