Computational Biology Gene Expression Intern

Bayer•Chesterfield, MO

18h•$23 - $48•Onsite

About The Position

We are seeking a motivated Computational Biology Intern 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.

Requirements

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;
Strong analytical thinking, scientific communication, and problem‑solving skills;
Ability to work independently and collaboratively in a multidisciplinary research environment.

Nice To Haves

Experience working with biological sequence data is preferred.

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.