| Project title | Description | PI | Co-PI | Eligibility |
|---|---|---|---|---|
| Carbon capture using microalgal routes | Carbon Capture from flu gas using microalgal reactors to convert carbon dioxide into value added products. | Pritha Chatterjee | Deepu J. Babu | Desired Bachelor’s Specialization - Civil Engineering, Environmental Engineering, Chemical Engineering. |
PhD
| Project title | Description | Keywords/remarks | PI | Co-PI | Eligibility |
|---|---|---|---|---|---|
| An Integrated Framework for Reconstructing High-Resolution Paleoclimate Data, Utilizing Numerical Models, Proxy Records, and Machine Learning | This project aims to reconstruct high-resolution global paleoclimate fields by integrating numerical model outputs with proxy records through statistical/machine learning/deep learning methods. | Downscaling, Data Assimilation, Deep Learning | Anamitra Saha | Chetankumar Jalihal | M.Tech / ME / MSc / MS in any discipline, and proficiency in Python programming is required. Priority will be given to candidates with a degree in a climate science-adjacent field. Prior experience working with climate model or paleoclimate datasets, and familiarity with deep learning methods, will be preferred. |
| Machine Learning Driven Coal Supply Chain Optimization under Uncertainty | India has a lot of coal reserves which can be utilized different manner (deriving chemicals out of it) from coal combustion. Such greener use of coal needs a supply chain for its optimial utilization in a large country like India. This project is going to explore in what ways AI/Ml can be utilized for optimally design and operate such a supply chain for Indian coal scenarios. | Data Analysis, Supply chain optimization, Uncertainty quantification, Bayesian optimization, Pareto analysis | Kishalay Mitra | Saptarshi majumdar | M.Tech / ME / MSc / MS in any discipline, and proficiency in Python programming is required. Priority will be given to candidates with a degree in a engineering field. Prior experience working with supply chain optimization, and familiarity with deep learning methods, will be preferred. |
| Development of a wind power forecasting methodology during wind ramps using multiscale simulations | Wind ramps are periods of rapid increase/decrease in the wind speed, typically occur during mornings and evenings. This project will utilize multiscale simulations (WRF- regional + LES-local) to predict wind speed, direction and power generation during such wind ramps. | Wind power forecasting, computational fluid dynamics, machine learning, dynamical downscaling | Niranjan Ghaisas | Vishal Dixit (IIT Bombay) | Masters degree in any discipline (e.g. Climate Sciences; Atmospheric Sciences; Mechanical/Chemical/Civil Engineering) or Bachelor's degree from an eligible institute is required. Interest to learn programming (in any language) is required. Prior experience working with copmutational fluid dynamics is desirable. |