SRIBS aims to become a leading center for cutting-edge work in theoretical sciences across diverse fields like biology, chemistry, physics, mathematics and computer science. While full-scale research initiatives are poised to commence with the onset of permanent faculty recruitment this year, the appointment of Dr. Suresh C. H. as Director signalspromising opportunities for groundbreaking exploration in theoretical and computational chemistry. Here are some prospective research areas and topics under consideration:
Theoretical and Computational Chemistry
- Materials Science and Nanotechnology: Designing and predicting novel materials with tailored properties for diverse applications.
- Excited states: Understanding photochemical reactions, fluorescence, and phosphorescence, using advanced time-dependent methods
- Reaction Mechanisms and Dynamics: Predicting and understanding reactions using theoretical and computational tools.
- Machine Learning (ML) for Chemistry: ML for data analysis, prediction, and design of molecules and materials.
- Developing new chemical theories and computational methods.
Theoretical and Computational Biology
- Protein Structure and Function: Develop and apply computational tools to predict protein folding, protein-protein interactions, and enzyme catalysis.
- Biomolecular Simulations and Modeling: Simulation and study of Biomolecules, membranes, and cellular phenomena at the atomic or molecular level.
- Machine Learning for Biological Data Analysis
- Computational Drug Discovery and Personalized Medicine: Leverage computational tools for drug design, target identification, and personalized medicine approaches based on individual genetic and phenotypic data.
Theoretical and Computational Physics
- Biophysics and soft matter: Simulating and analyzing biological systems, soft matter, and complex fluids using theoretical and computational tool.
- Computational Materials Science and Nanoscience
- Data science and ML: Apply machine learning to analyze experimental data, identify patterns, make predictions in physical systems.
- High-Performance computing and algorithm development
Mathematics and Computer Science
- AI-ML and Fundamental Mathematics:. Optimization and Machine Learning Theory, Probabilistic Graphical Models and Bayesian Networks, Topological Data Analysis and Machine Learning
- AI-ML Applications in Computational Biology, Chemistry and Physics.AI for Brain Mapping and Analysis, AI-Driven Modeling Systems Biology, AI-Powered Design of Materials, AI for Climate Modeling,
- AI for Soft Matter and Complex Systems
- High-Performance Computing