Computational Biology, Bioinformatics, and Biomedical Data Science Predoctoral Training Program

Contact: Dr. Veerasamy "Ravi" Ravichandran -- 301-451-9822 and ​Dr. Haluk Resat -- 301-827-6671

The goal of this program is to train Ph.D. students in the fundamentals and applications of computational and information sciences to gain insights and develop new strategies to solve problems relevant to basic biomedical research. Of particular interest are multi-disciplinary programs providing the skills to address biomedical research questions by utilizing large data sets and multiscale approaches. Accordingly, multi-department applications which partner biological sciences with quantitative and computational sciences (e.g., data science, computer science, statistics, mathematics, informatics, engineering) are encouraged. Training should include the use of theory, simulations, data sciences, machine learning, artificial intelligence, and other bioinformatics and computational approaches to address the full spectrum of basic research areas in the biomedical sciences, including for example, the fundamentals of analysis and interpretation of molecular sequence and structure, molecular function, cellular function, physiology, genomics, and genetics. In accordance with the NIH Strategic Plan for Data Science, training should also include aspects of fair and ethical data use, data sharing, and data security and confidentiality. NIGMS encourages programs to make use of resources and expertise available in the private sector to develop student skills and career paths in areas including efficient computer code development and use of emerging technologies and platforms.

Applications for a training grant in computational biology, bioinformatics, and data science should address the challenges of melding two disparate cultures, computing and biology, at both the faculty and student levels. These challenges include:

  • Creation of a collaborative infrastructure: Evidence for this infrastructure could include coauthored publications, collaborative research projects, joint service on dissertation committees, team teaching of courses and regular interactions in journal clubs and seminar series.
  • Training of graduate students from diverse scientific backgrounds: The proposal should address at least two scenarios for student success, involving students coming either from a biological background (with strong quantitative skills) or from a bioinformatics/computational/data science background.
  • Degree requirements: A successful training program should have a plan for tailoring the requirements in computational biology, bioinformatics, and data science training to avoid extending the time to degree.