PhD Research Opportunities 2025

Project Title Project Abstract Keywords / Remarks PI Co-PI Eligibility
Biomass to Biohydrogen for Industrial Application The project aims to develop technology for conversion of biomass to biohydrogen using a two step bioelectrolyzer. Electrode material, catalyst material, reactor design and pretreatment options will be explored. Renewable energy Pritha Chatterjee - BE/B.Tech/M.Tech/MSc in Climate Change, Civil Engineering, Chemical Engineering, Material Science, Microbiology, Biotechnology or Chemistry
Carbon Capture and Utilization using microbial or microalgal routes Biotechnological interventions to capture and utilize carbon dioxide (CO2) as a feedstock to create value-added products by harnessing the metabolic capabilities of microorganisms, thus providing promising avenues for mitigating climate change and creating greener bio-economic opportunities. The project involves developing energy efficient approaches for carbon capture from industrial emissions and its biological conversion to fuels, chemicals, materials and other novel products with the potential to drive the sustainable and carbon neutral circular economic opportunities. CCU Pritha Chatterjee - BE/B.Tech/M.Tech/MSc in Climate Change, Civil Engineering, Chemical Engineering, Material Science, Microbiology, Biotechnology or Chemistry
Integrated Framework for High-Resolution Paleoclimate Reconstruction This project aims to reconstruct high-resolution global paleoclimate fields by integrating numerical model outputs with proxy records through statistical/machine-learning methods. Downscaling Data Assimilation Machine Learning Anamitra Saha Chetankumar Jalihal Bachelor’s in Computer Science/Mathematics/Statistics, or Master’s in Civil (Water Resources)/Environmental/Computer Science Engineering, or Climate/Atmospheric/Environmental Sciences, or Mathematics/Statistics/Geoinformatics/Artificial Intelligence or related fields. Proficiency in programming is required.
Physics-Guided Machine Learning for Modeling Climate Risk This project will explore how machine learning models can be better informed by physics for improved modeling of risk of climate extremes. Downscaling Physics-Informed ML Climate Extremes Anamitra Saha - Bachelor’s in Computer Science/Mathematics/Statistics, or Master’s in Civil (Water Resources)/Environmental/Computer Science Engineering, or Climate/Atmospheric/Environmental Sciences, or Mathematics/Statistics/Geoinformatics/Artificial Intelligence or related fields. Proficiency in programming is required.
Advancing Early-Warning Systems through Hybrid Physics-ML Approaches This project aims to improve lead time, accuracy and resolution of early-warning of meteorological extremes through hybrid physics-machine learning approaches. Forecasting Physics-Informed ML Climate Extremes Anamitra Saha - Bachelor’s in Computer Science/Mathematics/Statistics, or Master’s in Civil (Water Resources)/Environmental/Computer Science Engineering, or Climate/Atmospheric/Environmental Sciences, or Mathematics/Statistics/Geoinformatics/Artificial Intelligence or related fields. Proficiency in programming is required.
From Lake Rejuvenation to Urban Water Resilience: A Systems Approach Integrating Connectivity, Legacy Nutrients, and Governance Investigate how surface–subsurface–stormwater connectivity controls the transport, storage, and remobilization of nutrients (especially phosphorus and nitrogen) within linked urban lakes and drains. Hydrologic connectivity legacy nutrients climate resilience Maheswaran R Ambika B.Tech/BE in Civil Engineering/Environmental Engg or Master's in Water/EE/RS
Fusing Multi-Source Satellites and Reanalysis datasets for studying deep convective systems This study investigates deep convective systems by fusing multi-source datasets from satellite missions such as GPM and INSAT with global reanalysis products. Leveraging their complementary strengths, the research aims to improve the characterization of convective structure, intensity, and lifecycle, advancing understanding of their spatial and temporal variability. - Shruti Upadhyaya - MTech in Climate Sciences, Atmospheric sciences, Water resources allied areas, computer sciences engineering.
Precipitation Nowcasting with AI/ML - - Shruti Upadhyaya - MTech in Climate Sciences, Atmospheric sciences, Water resources allied areas, computer sciences engineering.
AI/ML based Chemical Kinetic Modelling of Biodiesel, Ammonia and Other Renewable fuels Modelling the Combustion of alternative zero carbon fuels (like H2, NH3, bioethanol and biodiesel) and their integration into CFD codes is essential for the development of green propulsion systems. This project will use ANSYS Chemkin, ANSYS Fluent and in-house AI/ML based optimization and reduction codes to develop optimized and efficient chemical kinetic mechanisms for these fuels. Chemical Kinetics Green Fuels Hydrogen AI/ML Sayak Banerjee Kishalay B.E/B.Tech and/or M.E./M.Tech in Mechanical Engg./Chemical Engg./Aerospace Engg./Thermal Engg./Chemistry/Sustainable Engg./Climate Science and other allied areas. Experience in Chemical Kinetics/CFD/AI-ML implementation in optimization is preferred.
Robust Wind Farm Design under Uncertainty using AI/ML and optimization Uncertainty is associated with the nature of wind behavior. However, most of the wind farms do not consider this part while designing them. As a result, such farms do not generate power as claimed by designers. Through this project, we aim to quantify the uncertainty associated with wind behavior using various techniques (e.g. ML) and design farms which can predict and generate power in a robust fashion under various uncertain environment. Wind Farm Design AI/ML Uncertainty Kishalay Mitra - Bachelor’s in Computer Science/Electrical/Mechanical/Chemical/Mathematics/Statistics, and/or Master’s with some computational project with proficiency in programming. Knowledge in AI/ML will be added advantage
Optimization of Coal Supply Chain under uncertainty using Deep Learning Coal India runs a huge number of mines in the country which are supposed to supply various grades of coals to different clients in India and abroad. Meeting such demands in timely fashion leads to huge profit for the company which can otherwise lead to penalty to the company due to unnecessary disruption in energy generation at the client locations. Uncertainty associated with demand and many other parameters in the supply chain affect as well as make the optimal operation of the supply chain extremely challenging. In this project, we aim to run the supply chain in an optimal fashion in presence of various sources of uncertainty. Coal supply chain demand uncertainty AI/ML Optimization Kishalay Mitra - Bachelor’s in Computer Science/Electrical/Mechanical/Chemical/Mathematics/Statistics, and/or Master’s with some computational project with proficiency in programming. Knowledge in AI/ML will be added advantage
Characterization of Climate Variability of the Past El-Nino/La-nina constitute a dominant mode of variability in our climate. How this mode will evolve in response to global warming is not fully understood. In this project, we will use AI/ML to characterize El-Nino/La-Nina evolution during periods of natural global warming. The study will then be extended to other modes of climate variability - PDO, NAO etc. Climate Variability Natural global warming Paleoclimates Chetankumar Jalihal - Bachelor's in Computer science/Mechanical/Chemical/Civil/Electronics/Artificial Intelligence and/or Master's in any specialization under these fields. Master's in Atmospheric Sciences/Climate Sciences will be an added advantage. Good programming and analytical skills are required.
Extreme precipitation events of the past Extreme heavy precipitation events have severe socio-economic impact. These are likely to increase with global warming. We will explore the characteristics of these events (frequency, intensity, size) during the glacial periods. This project involves the use of high resolution climate model simulations and AI/ML techniques. Extreme precipitation events paleoclimates Climate models Chetankumar Jalihal Anamitra Saha Bachelor's in Computer science/Mechanical/Chemical/Civil/Electronics/Artificial Intelligence and/or Master's in any specialization under these fields. Master's in Atmospheric Sciences/Climate Sciences will be an added advantage. Good programming and analytical skills are required. Prior experience in climate modeling or handling climate data would be good.
Computing complex rainfall parameters using AI/ML Some rainfall related parameters are extremely challenging to calculate using the available data. We will use AI/ML to develop a framework for the calculation of these parameters. Convection Stability AI/ML Chetankumar Jalihal - Bachelor's in Mathematics/Computer science/Mechanical/Chemical/Civil/Electronics/Artificial Intelligence and/or Master's in any specialization under these fields. Master's in Atmospheric Sciences/Climate Sciences will be an added advantage. Good programming and Mathematical skills are required. Prior experience in climate modeling or handling climate data would be good.
Urban Climate Change: Impacts, Adaptation, Resilience Projects aiming to develop deeper understandings of impacts, adapations, resilience mechanisms of/to climate change in urban settings. Potential focuses include climate-health, climate justice, especially in relation to extreme heat. Qualitative and mixed methods approaches integrating spatial analysis are encouraged. Urban climate change adaptation impactsvulnerabilityresilience Aalok Khandekar - Bachelor's/master's in humanities and social sciences, architecture and urplan planning, climate change
Cycling of chemical contaminants under a changing climate Project will utilize environmental modeling at different scales to better understand the changes in cycling of chemical contaminants under a changing climate Chemicals climate Asif Qureshi - Bachelors or masters in, preferably an engineering or science field. Nevertheless, interest is required in computing
Greenhouse gas fluxes from ecosystems and their drivers Greenhouse gases are of extreme interest but still most of the focus is on CO2, while comparatively little is known about cycling and emissions of other more potent GHGs. This gap is particularly large in Indian ecosystems, where primary GHG emissions data from different ecosystems is lacking. This PhD work will focus on measuring GHG fluxes from differnet ecosystems in several to many locations in India, and try to undertand the underliying processes and drivers that proliferate or mitigate the GHG emissions. Overall, this project will also provide inventories of GHG emissions that may feed into local, regional and global models. GHG Asif Qureshi - First-class or division in Bachelor's (B.E./B.Tech/BSc) and Master's (M.E./M.Tech/MSc) degrees in Climate Change, Environmental, Civil, Chemical, or Mechanical Engineering,Environmental studies, environmental science, chemistry and allied fields. The student should be strong in basics and be resourceful, independent and dynamic in conducitng and leading fieldwork.
Developing a novel framework for assessing human vulnerability to climate change This work will combine data analysis and field survey to determing the direct and indirect impacts of climate change on human health. It is noted that while there are metrics for assessing ecosystem, coastal and related vulnerabilities, there is no integrated framework to quantify human vulnerability to climate chage. Thus, this work will also aim to develop a new quantitative framework to determine human vulnerability to climate change. The work would be multidisciplinary and may involve interactions with different stakeholds. clude climate-health, clima encouraged. Climate Vulnerability Asif Qureshi Anindita First-class or division in Bachelor's (B.E./B.Tech/BSc/BA) and Master's (M.E./M.Tech/MSc/MA/MPhil) degrees in Climate Change, Environmental studies, environmental science, social sciecnes and allied fields, chemistry and allied fields, civil, chemical, mechanical, environmental Engineering, computer sciecnes. Experience with, or willingness to do, fieldwork; and exposure to, or willingness to learn statistical techniques and quantitaitve aptitude are required.