Research Intern

Research Intern – Machine Learning for Material Science

Company: Shodh AI (Materials Science Division)

Location: Jaipur, Rajasthan

Position Type: 6-Month, Full-time, On-site

About Shodh AI

At Shodh AI (where 'Shodh' translates to Research), we are a deep-tech startup backed by the IndiaAI Mission, committed to building advanced AI systems for high-impact scientific and engineering domains. Our work spans Material Science, Robotics, STEM Education, and AI-driven Scientific Computing.

In our Materials Science division, we are developing AI models that accelerate the discovery, characterization, and design of next-generation materials. From predicting complex material properties using microstructures to generating novel material candidates through advanced generative models, our mission is to push the boundaries of computational material science using cutting-edge machine learning.

The Opportunity

We are seeking a highly driven Research Intern (Machine Learning – Materials Science) to join our scientific computing team. This role is ideal for someone who wants to work at the intersection of machine learning, deep learning, and computational materials research.

You will work directly with our research engineers and scientists to design, train, and optimize ML/DL models for microstructure-based property prediction, material generation, and physics-informed AI systems. This internship offers the opportunity to contribute to foundational R&D in a fast-moving, impact-focused environment.

What You'll Build

Material Property Prediction Models

You will develop ML/DL pipelines for predicting mechanical, thermal, and structural properties directly from microstructure images, simulation data, and material descriptors using CNNs, Vision Transformers, and GNNs.

Representation Learning for Microstructures

You will architect feature extractors and embedding models to learn high-dimensional latent representations of material microstructures, enabling downstream tasks like classification, clustering, and inverse design.

Generative Models for Material Design

You will experiment with VAEs, GANs, and Diffusion Models to generate hypothetical microstructures and explore the design space for novel materials with improved properties.

Scientific Data & ML Infrastructure

You will help build reliable, reproducible pipelines to preprocess microscopy data, manage datasets, and execute large-scale experiments on GPUs.

Physics-Informed and Scientific ML Systems

You will incorporate domain constraints, physics priors, and symmetry-preserving representations into ML models to ensure scientific validity and robustness.

Key Responsibilities

End-to-End Model Development

Own the full lifecycle of research features—from data preprocessing to model implementation, training, evaluation, and reporting.

Design, Experiment, & Optimize

Implement and rigorously test deep learning architectures (CNNs, ViTs, GNNs, Diffusion Models). Conduct ablations, hyperparameter optimization, and model benchmarking.

Ensure Reproducibility & Research Discipline

Write clean, modular, well-documented code. Use Git, experiment tracking tools, and maintain high-quality research logs.

Drive Technical Exploration

Propose new ideas, improvements, and experimental directions across generative modelling, microstructure representation learning, and physics-informed ML.

Collaborate & Communicate

Work closely with a multidisciplinary team of ML engineers, materials scientists, and computational researchers. Present results, insights, and technical findings clearly.

Who We're Looking For

Academic Foundation

Pursuing or recently completed a Bachelor's/Master's degree in:

Computer Science

Materials Science

Data Science

Metallurgical/Mechanical Engineering

Applied Physics

Or any related computational field

Technical Proficiency

Strong command of:

Python, PyTorch (preferred), TensorFlow

Deep learning concepts: CNNs, Transformers, Autoencoder

Scientific computing (NumPy, SciPy, Pandas)

Building and evaluating ML models on real-world datasets

ML & DL Fundamentals

Solid understanding of:

Optimization methods

Loss functions, regularization

Computer Vision concepts

Model evaluation metrics

Research-Oriented Mindset

Curiosity-driven, experimental, and comfortable reading research papers and implementing new ideas from literature.

Highly Advantageous Qualifications

Experience with materials datasets, microstructure analysis, or FEM/simulation data

Familiarity with GNNs, Diffusion Models, or Physics-Informed Neural Networks (PINNs)

Prior work with microscopy images, dataset curation, or domain-specific preprocessing

Understanding of crystallography, phase transformations, or structure–property relationships

Knowledge of CUDA, GPU optimization, or distributed training setups

Why Join Shodh AI?

Cutting-Edge Scientific Impact

Contribute to real scientific advancements in materials discovery using state-of-the-art ML techniques.

High Ownership, High Learning

You won't just "assist"—you will build, experiment, and innovate with autonomy.

Deep-Tech Startup Culture

Work in a fast-paced, intellectually driven environment where ideas move quickly from conception to implementation.

Mentorship & Research Visibility

Collaborate with experienced researchers and engineers, with opportunities for co-authoring papers, patents, and high-impact technical artifacts.

National-Level Mission

Be part of a startup backed by the IndiaAI Mission, contributing to India's scientific and technological growth.