Principal Machine Learning Scientist

The petitioner proposes to develop state-of-the-art machine learning methods for multiscale computational chemistry modeling to support the design and discovery of advanced materials and therapeutics. This work focuses on creating peptide, antibody, and small molecule therapies for treating cancer, obesity, and autoimmune diseases.

The petitioner proposes to develop advanced machine learning methods, cheminformatics tools, and modeling techniques to accurately predict molecular and chemical reaction properties. This work aims to accelerate the discovery of new molecules and streamline screening processes for pharmaceuticals, cosmetics, biofuels, and industrial dyes. The endeavor focuses on integrating predictive algorithms with cheminformatics models to assess novel molecules with high speed and accuracy.

The petitioner proposes to develop a chemistry-aware artificial intelligence (AI)-based platform to accelerate the discovery of life-saving medicines for chronic and neurodegenerative illnesses. This work involves applying computational modeling and machine learning techniques to design, synthesize, and test small molecules for clinical trial advancement.

The petitioner proposes to deploy artificial intelligence, including graph neural networks and large language models, to improve molecular design and chemical reaction prediction. The work focuses on designing eco-friendly synthetic pathways and enhancing yield prediction to reduce material waste in experimental chemistry and drug development.