Duration

Research internship tenure

Application Announcement

Applications officially open

Application Deadline

Last date to apply

Internship Start Date

Program commencement

Stipend

Financial support (if applicable)

Dr. Sudipta Roy

Self-Aware and Self-Reflective Neural Networks

Design neural architectures capable of estimating their own uncertainty, confidence, and failure modes, enabling safer deployment in real-world systems such as healthcare and autonomous systems.

Multimodal Scientific AI Models

Develop large multimodal models that integrate medical images, text, clinical reports, and tabular data to support scientific discovery and medical decision-making.

Self-Supervised Learning for Rare and Unknown Diseases

Design self-supervised learning approaches capable of discovering unknown patterns or rare conditions in medical imaging datasets without labeled data.

AI for Small Data and Data-Efficient Learning

Develop algorithms that can learn effectively from very small datasets, a critical challenge in healthcare and scientific domains.

Responsible and Ethical AI Systems

Develop AI models that are fair, interpretable, privacy-preserving, and robust, especially in sensitive domains such as healthcare.

Real-World Robust AI

Develop models that remain reliable under distribution shifts, noise, and real-world data variability.

Foundation Models for Medical Imaging

Develop large-scale medical foundation models trained on diverse imaging modalities (MRI, CT, ultrasound, X-ray) to enable generalizable medical AI systems.

Dr. Samik Mukherjee

Advanced Characterization of Materials for Optoelectronic and Nanoelectronic Applications

This project focuses on understanding semiconductor materials and nanostructures used in modern optoelectronic and nanoelectronic devices through advanced characterization techniques. Students from any background, with an interest in semiconductor materials and device physics are encouraged to apply.

During the internship, participants will learn how nanoscale structure, defects, and interfaces influence electronic and optical properties and device performance. The experience provides a strong foundation for students considering graduate studies or research careers in semiconductor physics, nanoelectronics, photonics, and advanced materials.

Synthesis of Perovskites, Modeling, and Device Optimization

This project explores the synthesis of perovskite materials and the modeling and optimization of devices based on them for next-generation energy applications. Students from any background, with an interest in semiconductor materials and device physics are encouraged to apply.

Interns will learn how material composition and processing influence electronic properties and device performance, and will gain exposure to both experimental materials development and device-level analysis. This experience will be particularly valuable for students planning further studies or research in optoelectronics, energy materials, semiconductor devices, and nanotechnology.

Dr. Mohna Chakraborty

Reasoning and Scientific Discovery with LLMs/VLMs

This research theme explores how Large Language Models (LLMs) and Vision-Language Models (VLMs) can assist in complex reasoning and accelerate scientific discovery. Participants will investigate methods that enable AI systems to perform structured reasoning, generate hypotheses, analyze scientific literature, and assist in research workflows.

The focus is on developing AI systems that can support knowledge discovery across domains such as healthcare, materials science, and computational research.

Human-Aligned and Ethical AI

This research theme focuses on developing AI systems that are transparent, responsible, and aligned with societal expectations. Participants will explore topics such as value alignment, fairness in AI systems, bias mitigation, governance frameworks, and responsible deployment of AI technologies in real-world settings.

Efficient Training and Next-Generation LLMs/VLMs

This research theme investigates methods to develop more efficient training strategies for large-scale language and vision-language models. Participants will explore innovations in model architecture, optimization techniques, parameter-efficient learning, and distributed training methods aimed at building next-generation AI systems that are both scalable and computationally efficient.

Grounded Intelligence for LLMs/VLMs

This research theme focuses on developing AI systems that integrate language and vision understanding with real-world knowledge and interaction. Participants will explore approaches such as multimodal learning, environment-aware AI systems, and integration with external knowledge sources to create models that can reason about and interact with the physical and digital world more effectively.

Dr. Vishnu Prasad

Brands in Large Language Models – Measuring and Improving How Brands Are Represented in LLMs for Personalization and GenAI Marketing

This research theme investigates how brands are represented, interpreted, and generated within large language models (LLMs). As generative AI increasingly shapes consumer information and brand discovery, understanding these representations becomes crucial for marketing strategy.

Participants will explore methods to measure brand visibility, sentiment, and positioning in LLM-generated content, as well as techniques to improve brand representation for personalization, recommendation systems, and generative AI–driven marketing applications.

Human–AI Opinion Dynamics – Studying Belief/Lexical Shifts from Sustained LLM Interaction on Marketing-Relevant or Contentious Topics

This research theme examines how sustained interactions with AI systems influence human beliefs, opinions, and language patterns over time. In marketing and consumer research contexts, AI-powered conversational agents increasingly shape how individuals perceive products, brands, and social issues.

Participants will study shifts in opinions, lexical choices, and decision-making behaviors resulting from repeated human–AI interactions, with particular attention to marketing-relevant topics and complex or contentious issues.

Metaverse – Dynamic World Models – Understanding Drivers of Engagement in Dynamic Metaverse-Based Retail/Service Contexts

As immersive digital environments evolve, the metaverse presents new opportunities for consumer engagement, retail experiences, and service innovation. This research theme focuses on understanding how dynamic virtual environments influence user behavior, brand engagement, and purchasing decisions.

Participants will explore models of consumer interaction within metaverse ecosystems, investigating factors such as digital identity, immersive experiences, and behavioral drivers in virtual retail and service platforms.

Agentic AI in Consumer Purchase Decisions – Examining How Autonomous AI Tools Shape Inclusive and Sustainable Purchase Decisions

The emergence of agentic AI, autonomous AI systems capable of planning and executing complex tasks, has significant implications for consumer decision-making. This research theme explores how AI agents can influence purchasing behavior, product discovery, and decision processes in digital marketplaces.

Participants will examine the role of AI-driven assistants in shaping inclusive and sustainable consumption patterns, as well as the broader implications of autonomous decision-support systems in modern commerce.

Application Process

Interested Applicants shall send a Synopsis / Statement on the Topic of Research and their CV by email to the dedicated email ID.

The email ID is being created and will be shared once active.