Research Scientist Agentic AI, Reinforcement Learning and Neuro-Symbolic Systems (f/m/div.)

About the role

As a research scientist in the semantic understanding and reasoning group (CR/AIR4) at Bosch Corporate Research, you will lead and advance research on intelligent AI systems that are able to take action, reason over goals and constraints, as well as organize knowledge through complex neuro-symbolic structures. Your work will focus on next-generation agentic systems that combine reinforcement learning, structured reasoning, memory, and knowledge-based representations to operate effectively in semantically rich also technically demanding environments. This role goes beyond individual technical contributions. You will contribute to shaping Bosch's scientific agenda in this area by identifying promising research directions, initiating and coordinating research activities, building connections to external academic and industrial partners, as well as representing Bosch in relevant research communities. You are expected to bring a strong external network and effectively position Bosch in collaborative projects, scientific exchanges, also strategic initiatives related to agentic AI, reinforcement learning, as well as neuro-symbolic systems. From a scientific perspective, you focus on developing systems that move from passive understanding toward goal‑directed behavior. You investigate how agents learn through interaction, simulation, and structured feedback, represent also manipulate knowledge in compositional forms, as well as integrate reinforcement learning with symbolic abstractions, hierarchical planning, memory, and reasoning. Your objective is to design systems that actively act while structuring knowledge to enable robust behavior, interpretability, also strong generalization. You will work closely with research scientists, engineers, students, as well as domain experts across Bosch. In addition to conducting high-level research, you will mentor students also junior researchers, actively shape and structure collaborative research activities, and contribute to the organizational development of this research area. Your work will be instrumental in establishing Bosch's long-term leadership in intelligent systems for complex technical environments. Education: excellent MSc in Computer Science, Machine Learning, Artificial Intelligence, Robotics, Systems Engineering, or related fields PhD in Machine Learning, Reinforcement Learning, Agentic AI, Neuro-Symbolic AI, Sequential Decision-Making, or a closely related area is mandatory ideally several years of post-PhD research experience in academia, industry research, or a comparable environment strong publication record in leading AI, machine learning, or autonomous systems venues such as NeurIPS, ICLR, ICML, AAAI, IJCAI, CoRL, RSS, AAMAS, ACL, EMNLP, KR, or similar Experience and Knowledge: Agentic AI, RL & Action‑Oriented Systems strong expertise in reinforcement learning and agentic AI, including sequential decision‑making and learning‑based planning experience with advanced RL paradigms such as model‑based, hierarchical, offline, multi‑agent, or constrained RL deep understanding of goal‑directed AI systems involving memory, tool use, planning, multi‑step reasoning, and long‑horizon behavior ability to design and analyze systems that act in complex environments and improve through interaction, simulation, or structured feedback Neuro‑Symbolic Systems & Knowledge Organization proven experience in combining learning‑based AI with symbolic or structured representations familiarity with neuro‑symbolic architectures, knowledge graphs, formal reasoning structures, and compositional representations ability to design systems that organize knowledge in semantically meaningful ways while supporting action, planning, interpretability, and generalization Systems Engineering & Structured Technical Domains interest in applying advanced AI methods to complex technical and cyber‑physical domains such as systems engineering, robotics, or industrial automation experience with structured engineering artifacts (e.g. requirements, system models, simulations, or formal specifications) is an advantage ability to frame complex technical challenges in terms of sequential decision‑making, planning, or knowledge‑based reasoning Scientific Leadership, Networking & Mentoring demonstrated ability to initiate, structure, and lead research activities in a focused technical domain strong external scientific network and experience building collaborations with academic and industrial partners proven track record in publications, project coordination, and community‑building activities mentoring experience with students and junior researchers, combined with strong organizational and coordination skills AI Infrastructure & Research Prototyping solid experience in Python and modern deep‑learning frameworks (e.g. PyTorch, TensorFlow, JAX) familiarity with scalable experimentation, reproducible research, and collaborative software development ability to translate research ideas into functional pro

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