Dr. Sudip Das

If you are interested in diving into the world of physics-informed machine learning (ML) to solve interesting problems in the emerging areas of Biophysics and Physical Chemistry (for details, see the Research section), please do reach out to me at: This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it.   Why Join Our Lab?   Work at the frontier of computational biophysics and AI, developing next-generation methods for drug discovery, catalysis, and biomolecular simulations. Tackle fundamental scientific challenges spanning proteins, enzymes, phase separation, membranes, and quantum chemical reactions with real-world biomedical and industrial impact. Gain expertise in cutting-edge computational techniques, including scientific programming, molecular dynamics, enhanced sampling, quantum chemistry (DFT, QM/MM), machine learning, and explainable AI. Contribute to the development of novel computational tools, such as AI-assisted transition-state discovery, dynamic and covalent docking platforms, and transferable quantum models. Collaborate across disciplines with computational and experimental researchers in biophysics, chemistry, biology, materials science, and data science. Build a strong, future-ready research profile for academia and industry through interdisciplinary training, high-impact publications, open-source software development, and national and international collaborations. Be part of an ambitious, curiosity-driven research environment that encourages innovation, scientific independence, and creative problem-solving from day one. Students from Physics, Chemistry, Biology, Mathematics, Biochemistry, Biotechnology, Bioinformatics, Computer Science, or related disciplines are encouraged to apply (please keep an eye on the SINP webpage for the same). While prior computational experience is advantageous, it is not essential—a curious and motivated mind is all you need to get started. Follow:  LinkedIn    X handle    Google Scholar    GitHub