AI Research Intern (Time Series Foundation Models)

Quick Facts

• Role: AI Research Intern (Time Series Foundation Models)

• Focus: Multimodal and sensor-rich foundation models for heterogeneous real-world data

Description

You will research how AI systems learn generalizable representations from heterogeneous data, including time series, audio, vision, and physical sensor signals, and transfer knowledge across tasks and domains. Working with Bosch researchers, you’ll explore topics such as foundation models for time-series and multimodal data, causal/evidence-based reasoning, shared representations across modalities, self-supervised learning from unlabeled sensor data, multimodal fusion, and robust learning under missing/noisy measurements and distribution shifts. You’ll formulate research questions, develop and evaluate methods, build experimental prototypes, and may publish results or deliver working prototypes.

Role Responsibilities

• Formulate research questions in the area of multimodal foundation models and real-world sensing

• Develop and evaluate new methods

• Build experimental prototypes

• Collaborate with researchers and domain experts to study behavior on real-world problems

• Produce strong research results that may lead to publications and prototypes

Requirements

• Currently pursuing a Ph.D. in a relevant field (CS, EE/CE, ML, AI, or related)

• Research experience with time-series foundation models, multimodal models, or self-supervised learning

• Hands-on experience with PyTorch or similar frameworks

• Research/project experience in multimodal learning, time-series modeling, representation learning, foundation models, or signal processing

• Ability to design experiments and analyze model behavior

• Strong Python programming skills

• Ability to communicate and collaborate

Benefits

• Opportunity to turn research ideas into publications and working prototypes

• Access to challenging datasets and applications across mobility, manufacturing, robotics, and intelligent systems