IBM Research internship - AI for Science (3 months)

Introduction

IBM Research takes responsibility for technology and its role in society. Working in IBM Research means you'll join a team who invent what's next in computing, always choosing the big, urgent and mind-bending work that endures and shapes generations. Our passion for discovery, and excitement for defining the future of tech, is what builds our strong culture around solving problems for clients and seeing the real world impact that you can make. IBM's product and technology landscape includes Research, Software, and Infrastructure. Entering this domain positions you at the heart of IBM, where growth and innovation thrive.

Your Role And Responsibilities

Our research team is seeking a highly skilled and motivated intern for the Spring/Summer of 2026. The ideal candidate will possess a strong foundation in AI and Machine Learning, with expertise in Foundation Models and multimodal data analysis.

The internship will focus on advancing the capabilities of foundation models for scientific and physical systems, with emphasis on multimodal learning pipelines that combine time-series, spatial, and video data. This integration will enhance the model's accuracy by incorporating domain-specific insights critical to understanding the system's dynamics.

The Project Will Involve The Following Key Activities

Foundation Model Development: extending and fine-tuning foundation models to handle multi-modal data inputs. This includes developing techniques to ensure the model effectively captures and utilizes the distinct characteristics of each data modality and adopts to unseen before tasks.

Physics-based and Simulation-informed Modeling: incorporate domain constraints and PDE-inspired priors to improve model robustness and generalization, and develop strategies to encode control parameters alongside sensor data to capture causal relationships.

Preferred Education

Master's Degree

Required Technical And Professional Expertise

PhD/Master candidate in Engineering, Computer Science, Math or similar fields. A solid background in machine learning and optimization.

Familiarity with foundation models and their applications in data science.

Experience with multi-modality data, particularly time-series and spatial data.

Strong programming skills, especially in Python or similar languages.

Preferred Technical And Professional Experience

Familiarity with Fondation models for Science.