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Rational vaccine design using computational models: The past few years has seen an abundance in sequence data for viruses such as SARS-CoV-2, HIV and HCV. This information can be exploited for the rational design of vaccines, by building models which capture the patterns in the viral sequences. These patterns can be exploited to find weaknesses in the virus, which can then be used to design effective vaccines.

Developing a machine learning model to predict disease outcome: Accurately predicting disease outcomes can have a significant impact on patient care, leading to early detection, personalized treatment plans, and improved clinical outcomes. Machine learning algorithms provide a powerful tool to achieve this goal by identifying novel biomarkers and drug targets for various diseases. By integrating machine learning algorithms with biological data, you will have the opportunity to push the boundaries of precision medicine and contribute to algorithms that can revolutionize the field. Such biological data includes single-cell datasets, where we have considerable experience.