Computational Biology Intern

We are seeking a qualified intern candidate to join our computational biology team on a 3–6-month project. This individual will have prior artificial intelligence “AI” and machine learning “ML” experience working with large data sets on a biological problem within the drug development lifecycle. You will be given the opportunity to contribute to Garuda’s groundbreaking work while learning different drug genomic integrity principles. This is a 40-hour per week paid internship position that will begin in May 2025 and duration can be up to 6 months.

Role and Responsibilities:

• Develop and apply AI/ML models for analyzing genomic, transcriptomic, and epigenomic data to assess stem cell stability and drug product integrity
• Manage and curate large datasets, ensuring data integrity and accessibility
• Interpret results and provide actionable insights to support ongoing research projects
• Develop, maintain, and optimize bioinformatics pipelines for data processing and analysis
• Ensure pipelines are scalable, efficient, and reproducible
• Work closely with biologists, clinicians, and other researchers to understand their bioinformatics needs and provide appropriate solutions
• Communicate findings through written reports and presentations
• Participate in team meetings and contribute to the planning and execution of research projects
• Assist on AWS based S3 to S3 and SFTP transfers of big data
  
  

Qualifications:

• Must be enrolled in either a Master’s or PhD degree program with a focus in Bioinformatics, Computational Biology, Genomics, Computer Science, or a related field
• Strong experience in machine learning, deep learning (TensorFlow or PyTorch or Scikit-learn), and computational biology is required
• Familiarity with AWS and cloud-based bioinformatics solutions is required
• Proficiency in programming languages such as Python, R, and Bash is preferred
• Prior research experience in stem cell (ideally iPSC) genomics or computational drug discovery is preferred
• Experience in developing AI-driven variant classification models and knowledge of graph-based or transformer models for genomic data analysis is also preferred.
  
  

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