Research Scientist - Cyber-Physical AI & Reasoning

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Cyber-Physical AI and Reasoning (Engineer / Researcher) The Cyber-Physical AI and Reasoning group at Bosch Research Pittsburgh develops intelligent systems that tightly integrate learning, reasoning, perception, and physical interaction . Our mission is to build safe, robust, and adaptive cyber-physical systems that operate reliably in real-world environments—spanning robotics, automation, manufacturing, and intelligent devices. We focus on systems that combine data-driven learning with structured models, physical constraints, and embedded intelligence , enabling machines to sense, decide, and act across diverse scenarios while continuously improving over time, including through interaction with humans. Core Research & Development Areas Our work spans a broad range of Cyber-Physical AI topics, including but not limited to: Embodied and Cyber-Physical AI Robot learning and control in physical environments Dexterous manipulation and automation for manufacturing Human–machine interaction and shared autonomy

Hybrid and Model-Based AI Combining learning-based models with physics-based, symbolic, or optimization-based components World models, state estimation, and system identification Safety-aware and constraint-driven learning and control

Multimodal & Foundation Models Vision-Language(-Action) models for perception, planning, and control Representation learning across modalities (vision, language, proprioception, signals) Cross-domain and cross-embodiment generalization

Cyber-Physical Systems & Embedded Intelligence Embedded ML and edge AI for real-time systems Integration of learning algorithms with sensors, actuators, and control stacks Sim-to-real transfer and deployment on physical platforms

Engineering & Prototyping System prototyping Data collection pipelines, simulation environments, and benchmarking frameworks Deployment of AI systems to industrial settings

Role & Responsibilities

Depending on background and seniority, candidates will contribute to a mix of research and engineering activities , including: Defining and investigating compelling problems in Cyber-Physical AI & Reasoning Designing, implementing, and evaluating learning-based or hybrid AI systems Conducting literature reviews and translating insights into practical system designs Developing experimental pipelines (simulation, real-world testing, data collection) Analyzing system performance, robustness, safety, and failure modes Collaborating with interdisciplinary teams spanning AI, robotics, and engineering Contributing to: Research publications and technical reports Industrial patents and technology transfer Prototypes deployed in labs or production environments

Technical Experience & Skills We welcome candidates with overlapping subsets of the following skills—depth in all areas is not required: Cyber-Physical Systems & Robotics State estimation, system modeling, or dynamics Safety, robustness, or generalization in physical systems Robot perception, control, planning, or manipulation

Engineering & Systems Embedded systems, real-time systems, or edge AI Integration of ML models with hardware, sensors, and control software Experience with simulation tools, robotics middleware, or control stacks

Machine Learning & AI Multimodal learning, representation learning, or foundation models Reinforcement learning, imitation learning, or optimal control Hybrid approaches combining data-driven and model-based methods (e.g., neuro-symbolic integration)

Practical ML & Experimentation Training and evaluating neural models (single- or multi-GPU) Data curation, dataset analysis, and benchmarking Debugging non-convex optimization and real-world system failures

Minimum Qualifications

Master’s or Ph.D. in Computer Science, Robotics, Electrical/Mechanical Engineering, Machine Learning, or a related field Strong foundation in AI/ML, cyber-physical systems, robotics, control Experience with programming and experimental system development Preferred Qualifications Experience with physical or robotic hardware systems Experience with embedded or real-time systems Experience with multimodal foundation models Exposure to hybrid or model-based AI methods Prior research publications, technical reports, or strong project portfolios Experience collaborating in interdisciplinary or industrial research teams Who Should Apply This role is well-suited for: Early-career researchers seeking hands-on experience in Cyber-Physical AI Candidates interested in bridging AI research and real-world engineering Researchers and engineers excited about deploying AI systems beyond simulation

All your information will be kept confidential according to EEO guidelines.