AI Research Intern – Time Series Foundation Models

AI Research Intern – Time Series Foundation ModelsInternLegal Entity: Robert Bosch LLCCompany DescriptionThe Bosch Research and Technology Center North America with offices in Sunnyvale, California, Pittsburgh, Pennsylvania, and Cambridge, Massachusetts is a part of the global Bosch Group (www.bosch.com), a company with over 70 billion euro revenue, 400,000 employees worldwide, a very diverse product portfolio, and a history spanning over 125 years. The Research and Technology Center North America (RTC-NA) is dedicated to providing technologies and system solutions for various Bosch business fields, primarily in the field of artificial intelligence, energy technologies, internet technologies, circuit design, semiconductors and wireless, as well as advanced MEMS design.As a part of the global research, our AI research in Silicon Valley focuses on Foundation Models, Big Data Visual Analytics, Explainable AI (XAI), Natural Language Processing, Computer Vision & Mixed Reality, Cloud Robotics, Data Science, AI System Engineering, Time-series Analysis. We develop scalable, intelligent, and trustworthy AIoT solutions for Bosch products and services in application areas such as automated driving, advanced driver assistance systems (ADAS), robotics, smart manufacturing, enterprise AI, health care, smart home and building solutions.Job DescriptionWe 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 OnYour research may explore topics such as:Foundation models for time-series and Multimodal dataCausal Analysis and evidence-based reasoningLearning shared representations across images, time series, and other sensing modalitiesSelf-supervised learning from large-scale unlabeled sensor datasetsMultimodal fusion and cross-modal representation learningPre-training strategies for heterogeneous and asynchronous dataGenerative modeling and synthetic data for sensor-rich applicationsAdapting pretrained models to new sensors, tasks, and domains with limited labeled dataRobust learning under missing modalities, noisy measurements, and distribution shiftsEfficient multimodal models for real-world and edge deploymentNew architectures and evaluation methods for multimodal foundation modelsYou 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.QualificationsBasic Qualifications:Currently pursuing a Ph.D. in Computer Science, Electrical/Computer Engineering, Machine Learning, AI, or a related fieldResearch experience with time-series foundation models, multimodal models, or self-supervised learningHands-on experience with PyTorch or similar frameworksResearch or project experience with multimodal learning, time-series modeling, representation learning, foundation models, or signal processingComfortable designing experiments and analyzing model behavior rather than only applying existing modelsStrong Python programming skillsAbility to communicate research ideas clearly and work collaborativelyPreferred Qualifications:Experience working with real-world sensor and telemetry signals from radar, LiDAR, IMU, or other multivariate time seriesExperience with Transformers, generative models, contrastive learning, or cross-modal learningExperience adapting large pretrained models through fine-tuning, parameter-efficient methods, or transfer learningFamiliarity with robustness, domain adaptation, missing-modality learning, or uncertainty estimationPublications at venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICASSP, AAAI, or related conferencesOpen-source implementations or research projects demonstrating strong experimental skillsAdditional InformationEqual Opportunity Employer, including disability / veterans\*Bosch adheres to Federal, State, and Local laws regarding drug-testing. Employment is contingent upon the successful completion of a drug screen and background check. Candidates who have been offered the position must pass both screenings before their start date.BOSCH is a proud supporter of STEM (Science, Technology, Engineering & Mathematics)FIRST Robotics (For Inspiration and Recognition of Science and Technology)AWIM (A World In Motion)The U.S. base salary range for this intern position is$55.00-$70.00hourly. Within the range, individual pay is determined based on several factors, including, but not limited to, type of degree, work experience and job knowledge, complexity of the role, type of position, job location, etc. Your Hiring Manager can share more details about the specific salary range for this position during the interview process.For more information on our culture and benefits, please visit:Culture and Benefits | Bosch in the USABy clicking the link above or any third-party link within this posting, you are leaving this site and going to a third-party website where the third-party website's terms and privacy policy applyI'm interestedI'm interestedPrivacy NoticeImprint