Gene Expression & Genomics Intern – ML for Crop Design

Bayer AG; Chesterfield (MO); On-site; USD 31,000 - 66,000; Full time

Benefits offered by this job

Health care

Retirement plan

Paid time off

Job summary

Bayer AG is seeking a motivated Computational Biology Intern to help build and analyze large DNA sequence datasets and evaluate gene expression prediction technologies. You will apply machine learning approaches to improve expression element design across crop species, collaborating with a multidisciplinary team of scientists.

The role offers exposure to genomic data analysis, development of reproducible workflows, and opportunities to present findings to stakeholders in a dynamic research

Qualifications

• Pursuing a Master’s or Ph.D. in a related field.
• Experience with Python or R for data analysis.
• Interest in gene regulation and regulatory genomics.

Responsibilities

• Curate, integrate, and analyze coding and non-coding sequence datasets.
• Develop datasets for training and evaluating machine learning models for gene expression prediction and design.
• Identify crop-specific sequence patterns linked to expression outcomes.
• Evaluate tools and technologies for annotating and predicting DNA sequence function.
• Develop reproducible analysis workflows and document data processing methods.
• Create visualizations and summaries communicating insights to teams.
• Present findings and recommendations to multidisciplinary stakeholders.

Skills

Python

R

Data analysis

Machine learning

Education

Master’s or Ph.D. in Bioinformatics/Computational Biology

Job description

Bayer AG is seeking a motivated Computational Biology Intern to help build and analyze large DNA sequence datasets and evaluate gene expression prediction technologies. You will apply machine learning approaches to improve expression element design across crop species, collaborating with a multidisciplinary team of scientists.

The role offers exposure to genomic data analysis, development of reproducible workflows, and opportunities to present findings to stakeholders in a dynamic research