Our department offers Summer Fellowship Program (SFP) for B.Tech students who have completed their 6th semester. Besides the SFP, our department also offers internships under MoUs with NITs, IIITs and other CFTIs. Students selected for the internship from CFTIs are given an option to do their final year project in our department based on their internship performance, subject to the clauses related to course work requirements under the MoU.

The list of projects offered via the internship program for the summer of 2025 is in the tab below. If you are interested in applying to one of the projects, please note the Project ID in the first column and mention this in your application.

If you have queries about the SFP or the internship under MoUs with CFTIs, please get in touch with apoffice@iitm.ac.in.

In case of difficulty viewing the below table, please click here to view the spreadsheet.

Project ID	Faculty name	Project title	Description	Type of project
SRIP-01	Shuvrangsu Das	Inferring material properties from experimental displacement fields	Identification of heterogeneous material properties, which vary spatially in specimens, is a critical challenge in solid mechanics. A new paradigm, termed Material Testing 2.0, is recently proposed that combines experimental displacement fields and inverse modeling framework to extract heterogeneous constitutive parameters. Identification of material properties need to be posed as an inverse problem given the experimental displacement fields. In this work, we will focus on specimens made of a few distinct phases, whose spatial distribution and material properties are a-priori unknown. For this purpose, we will pursue the techniques of inverse modeling, both from classical and data-driven perspectives.	Theoretical/Numerical/Computational/Analytical
SRIP-02	Ilaksh Adlakha	Data Driven Computational Solid Mechanics	Next-generation structural design demands understanding mechanical variability from heterogeneous microstructures, requiring extensive datasets that are computationally expensive. Low-rank approximation (LRA) methods offer a solution by developing reduced-order models that approximate mechanical properties using smaller datasets. This work focuses on advancing LRA methods to predict variability in local and overall mechanical behavior caused by material heterogeneities. These methods significantly reduce computational costs compared to finite element analyses and contemporary data science techniques. By leveraging mechanics-informed tools, efficient large-scale data collection is facilitated, thus enabling a deeper understanding of microstructure-driven mechanical variability.	Theoretical/Numerical/Computational/Analytical
SRIP-03	S.Vengadesan	Data driven modelling of Electrohydrodynamics	Electrohydrodynamics (EHD) find applications Solid-liquid melting, drop/bubble dynamics, enhanced heat transfer and electrospinning. All these real life engineering problems are with multiple parameters / variables like Capillary number, density / thermal Bouyancy, Surface tension, viscosity ratio etc. For any specific application, arriving at optimized set of parameters is a challenge. Data-driven modelling approach provide a economical solution. In this project, the objective is to develop a deep learning neural network framework to address this problem.	Theoretical/Numerical/Computational/Analytical
SRIP-04	Kiran Raj M	Development of high throughput droplet microfluidic systems	High-throughput droplet microfluidic systems are transformative technologies enabling precise control and manipulation of individual droplets at micro and nano scales. They find applications in biological, chemical, and material sciences. The project aims to develop a prototype to carry out multiplexed handling of a large number of droplets inside a microfluidic system, to be handled using a custom made GUI. To validate the results and optimize for maximum efficiency, a CFD analysis is also one of the key objective in the study.	Both
SRIP-05	M Manivannan	Perception Engineering in XR(ARVR/MR) and Haptics	Perception Engineering is a new field of engineering that we are promoting in which the measures of perception is used to design and development several solution for Augmented Reality, Virtual Reality, Mixed Reality and Haptics. Research involved in this new field of engineering involves both hardware and software, both theoretical and experimental.	Both
SRIP-06	Danny Raj M	Towards intelligent decisions in autonomous robots	If autonomous vehicles do not think like Indian drivers, they are not going to make it to Indian roads. Simple lane following, object detection and maneuver, are insufficient. To drive on Indian roads, the vehicle should be able to not only detect other vehicles but also predict the trajectories of these vehicles and be able to “think” of optimal maneuvers on the go. In this project, we want to use AI to learn how robots should move optimally, from the bottom up, in a variety of contexts.	Both
SRIP-07	Amit Nain	Smart Biomaterials for Tissue Engineering Applications	Smart biomaterials have the potential to transform human health. For example, natural biopolymers, like chitosan and alginate, come with inherent biocompatibility, biodegradability, and film/gel-forming properties. They can be precisely manipulated to achieve desired properties through solvent casting, 3D printing, and even 4D printing. Chitosan, for instance, offers a unique piezoelectric effect (generating electricity from pressure) and shape-shifting abilities. Alginate complements this with its gelling and ionic crosslinking properties. These base biopolymers are further empowered by incorporating nanoengineered bionanomaterials, which introduce a new level of control and functionality.	Experimental
SRIP-08	S Ganga Prasath	Markerless tracking of elastic structures using Generative AI	The project focuses on using generative AI to enable markerless tracking of elastic structures. By generating simulated data and integrating it with textures, the goal is for deep learning models to identify and track deforming objects without physical markers. In the first part, the goal is to create realistic motion models and to accurately capture deformation, strain, and displacement. In the second part, we would like to combine it with computer vision, physics-based simulations, and AI inference resulting in a precise and scalable solution for real-time monitoring of elastic structures.	Both
SRIP-09	Satyanarayanan Seshadri	Application of AI tools in Net Zero	Explore both conventional models and GenAI tools in the net Zero domain. This project aims to develop a base understanding of the emerging research and industry trends and also develop a few use cases in the AI4Net0 group	Theoretical/Numerical/Computational/Analytical
SRIP-10	Prasad Patnaik BSV	Computational Fluid Dynamics (CFD) aided design and development of Mechanical devices for the human heart-lung systems	Development of alternative solutions to a non-functional heart and lung systems are primarily aided by the Mechanical Engineering based designs. If such devices have to be cost effective, they can easily benefit the patients as well as the cardiovascular and thoracic surgeons. Development of these bio-mechanical devices would necessitate the use of Mathematical/ computational modelling tools. The proposed Computational Fluid Dynamics (CFD) based simulations will develop modelling aided designs for the mechanical heart pump (popularly known as Left ventricular assist device – LVAD) as well as extracorporeal membrane oxygenation (ECMO). Only basic Fluid Mechanics background is required.	Theoretical/Numerical/Computational/Analytical
SRIP-11	Shaikh Faruque Ali	Data driven dynamical modelling	Dynamic model of a structure has to be established from experimental observation. Experimental data is already available.	Both
SRIP-12	Sarith P Sathian	Computer Simulation of neurons and synaptic activity	Neurons are the fundamental units of the human nervous system. For tasks such as pattern recognition and navigation, the human brain operates with remarkably low power consumption, typically only a few watts. In contrast, modern CPU and GPU-based hardware require thousands of watts to achieve similar results. This significant disparity in power consumption prompts the question: what accounts for this gap? To understand this, it is essential to examine the system-level architecture of biological versus artificial neural systems. In biological neurons, memory and processing functions are integrated within each neuron. Additionally, the human brain contains approximately 100 billion neurons, each connected to about 10,000 others, creating an extensive network of parallel connections. This makes biological systems far more efficient than conventional complementary metal-oxide-semiconductor (CMOS) circuits. CMOS devices, which typically use the von Neumann architecture, require constant data transfer between memory and processing units, leading to performance bottlenecks. To address this, researchers are developing neuromorphic devices aimed at reducing power consumption without sacrificing performance.The rapid advancement in artificial intelligence and machine learning necessitates alternatives to complementary metal-oxide-semiconductor (CMOS) devices to efficiently handle large datasets. Memristors, introduced by Leon Chua as the fourth fundamental circuit element, are promising candidates due to their in-memory computation and parallel data processing capabilities, mimicking the synaptic behavior of the human brain. Fluidic-based memristors, however, more closely resemble biological synaptic behavior than their solid-state counterparts, which rely on electrical pulses. Fluidic-based memristors function similarly to the chemical synapses in neurons. Our research project aims to investigate the use of TiO2-based fluidic-memristors for neuromorphic applications. This involves confining TiO2 nanoparticles within a graphene-based nano-fluid slit, where the nanoparticles act as memory retention units, similar to the role of electrons in CMOS devices. The study involves massive computations using supercomputers and the use of AI/ML tools.	Theoretical/Numerical/Computational/Analytical
SRIP-13	Swathi Sudhakar	Nanaotherapeutics for Cancer	Nanotherapeutics revolutionize cancer treatment by leveraging nanoscale materials for targeted therapy, enhanced drug delivery, and reduced side effects. These systems, including liposomes, dendrimers, nanoarchaeosomes, and quantum dots, offer precise tumor targeting through surface modifications and responsive drug release. Nanoarchaeosomes, for instance, exhibit thermostability and immune modulation, making them ideal for cancer vaccines. We are looking for some students to design nanotherapeutics enable combination therapies by co-delivering drugs and imaging agents, aiding real-time tumor monitoring.	Experimental
SRIP-14	Lakshminath Kundanati	Structural and Mechanical characterization of the exterior coating layer of a Beetle eye lens	Nature offers a variety of microstructures that inspire the development of innovative optical designs. Insect eyes, such as those of flies, bees, and subterranean beetles, exemplify this by reducing pollen and dust adhesion. These surfaces hold immense potential for engineering applications. However, while their surface morphology has been extensively studied, little is known about their structural, chemical, and mechanical properties. The plan is to study Rhinoceros Beetles eyes coatings because they appear transparent, wear-resistant, and self-cleaning due to their habitat. This preliminary study will analyze the eye’s cross-section for structure, composition, and some physical properties.	Experimental
SRIP-15	Kannabiran Seshasayanan	Transitions between turbulent flows	Transition between laminar to turbulent flows are fairly understood with theoretical and experimental studies being in good agreement with each other. In many geophysical and astrophysical systems, transitions are observed between turbulent flows. The theoretical aspects to understand such transitions is not developed. The main questions being: 1. what triggers such transitions?, 2. can they be understood as an instability process?, 3. whether a phase transition picture explains such phenomena?, these are some of the questions that one would like to answer to better understand such phenomena. In this project we will aim to study such transitions with the help of simulations and analytical techniques.	Theoretical/Numerical/Computational/Analytical
SRIP-16	Arockiarajan	Morphing wing for UAV and structural health monitoring with machine learning techniques	The integration of morphing structures, particularly camber morphing wings, represents a significant advancement in aerospace engineering. These wings can adapt their shape to optimize aerodynamic performance across various flight conditions. Distributed sensing techniques enhance this adaptability by providing real-time data on structural integrity and performance. Implementing structural health monitoring (SHM) ensures that these morphing wings maintain their safety and functionality, detecting potential issues before they escalate. Furthermore, the application of machine learning algorithms allows for predictive analysis and optimization of morphing strategies, enabling smarter, more efficient designs that respond dynamically to changing flight environments and operational demands.	Both
SRIP-17	Sayan Gupta	Oscillator based computing	Biological systems, including the human brain, communicate with each other through oscillatory signals. The power consumed in human brains is a fraction of the power consumed in computing indicating that the current paradigm of computing may not be most efficient. This has led to focus on oscillator based computing, and requires understanding of the dynamics of coupled nonlinear dynamical systems.	Theoretical/Numerical/Computational/Analytical
SRIP-18	A P Baburaj	Estimating earths heat flux from coherent structures	Radar images of earth's surface show lines of hot rising air. Similar lines of plumes are seen in convection from a horizontal hot surface in laboratory. Theoretical analysis of the laboratory situation has been done by our group, from which the heat flux can be estimated from the lengths of these lines. The project aims to extend this analysis to the case of earth, which would enable to estimate the heat from earths surface from radar images. Heat flux from earths surface is a major unknown in climate models, predicting which will help in predicting the climate better	Theoretical/Numerical/Computational/Analytical
SRIP-19	N. Sujatha	Development of optical pattern acquisition module for biological studies	This project involves the development of a device for capturing precise optical response patterns from biological samples and associated data analysis. The intern would be involved in the device design, implementation, and data analysis.	Both