AI Research Intern – Time Series Foundation Models

We are looking for research interns interested in advancing foundation models for multimodal and sensor-rich environments. Our research explores how AI systems can learn generalizable representations from heterogeneous real-world data, including time series, audio, vision, and physical sensor signals, and transfer that knowledge across tasks and domains.

As an intern, you will work alongside Bosch researchers on open research problems at the intersection of multimodal learning, foundation models, and real-world sensing. You will have access to challenging datasets and applications from areas such as mobility, manufacturing, robotics, and intelligent systems, with opportunities to turn research ideas into publications and working prototypes.

What You’ll Work On

Your research may explore topics such as:

• Foundation models for time-series and Multimodal data
• Causal Analysis and evidence-based reasoning
• Learning shared representations across images, time series, and other sensing modalities
• Self-supervised learning from large-scale unlabeled sensor datasets
• Multimodal fusion and cross-modal representation learning
• Pre-training strategies for heterogeneous and asynchronous data
• Generative modeling and synthetic data for sensor-rich applications
• Adapting pretrained models to new sensors, tasks, and domains with limited labeled data
• Robust learning under missing modalities, noisy measurements, and distribution shifts
• Efficient multimodal models for real-world and edge deployment
• New architectures and evaluation methods for multimodal foundation models
You will formulate research questions, develop and evaluate new methods, build experimental prototypes, and work with Bosch researchers and domain experts to understand how these approaches behave on real-world problems. Strong research results may lead to publications at leading ML, AI, or signal-processing venues.

Function: Engineering
Experience Level: Internship