Computational Biology Gene Expression Intern

About the role

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.

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;
• Benchmark gene expression prediction and annotation technologies;
• Develop data‑driven approaches to improve expression element selection and optimization.

Qualifications

• Pursuing a Master’s or Ph.D. degree in Bioinformatics