Computational Biology Gene Expression Co-Op

About the role

We are seeking a motivated Computational Biology Co‑Op to support the development of next‑generation gene expression design capabilities. This role will focus on building and analyzing large‑scale coding and non‑coding DNA sequence datasets, evaluating gene expression prediction technologies, and applying machine learning approaches to improve expression element design across crop species. The successful candidate will work with computational biologists, genomics scientists, and gene design researchers to generate datasets, benchmark predictive models, and identify biological patterns that influence gene expression.

Location: United States, Missouri, Chesterfield (Crop Science Division).

This posting will be available for application until at least March 19, 2027.

Responsibilities

• Curate, integrate, and analyze coding and non‑coding sequence datasets;
• Develop datasets suitable for training and evaluating machine learning models for gene expression prediction and design;
• Identify crop‑specific sequence patterns associated with expression outcomes across diverse datasets;
• Evaluate current computational tools and technologies used to annotate, score, and predict the function of coding and non‑coding DNA sequences;
• Develop reproducible analysis workflows and document data processing methodologies;
• Create visualizations and summaries to communicate biological and computational insights to research teams;
• Present project findings and recommendations to multidisciplinary stakeholders;
• Potential project areas include benchmarking gene expression prediction and annotation technologies, and developing data‑driven approaches to improve expression element selection and optimization.

Qualifications

• Pursuing a Master’s or Ph.D. degree in Bioinformatics, Computational Biology, Genetics, Genomics, Data Science, Computer Science, Plant Biology, or a related field;
• Experience with data analysis using Python and/or R;
• Familiarity with genomics, transcriptomics, machine learning, or statistical modeling;
• Interest in gene regulation, regulatory genomics, AI applications in biology, and genetic design;
• Experience working with biological sequence data is preferred;
• Strong analytical thinking, scientific communication, and problem‑solving skills;
• Ability to work independently and collaboratively in a multidisciplinary research environment.

Pay

Employees can expect to be paid a salary of approximately between $22.75 to $47.75. Additional compensation may include a bonus or commission (if relevant).

Benefits

Additional benefits may include health care, vision, dental, retirement, PTO, sick leave, etc (if relevant).