{"id":204954,"title":"PhD - Neurosymbolic Reasoning for Domain-Adaptive Generative AI","company":"Bosch Group","city":"Renningen","region":"Baden-Württemberg","location":"Renningen, BW, Germany","work_model":"unknown","employment_type":"full_time","employment":["thesis"],"roles":["data_science"],"visa":"not_mentioned","relocation":"not_mentioned","posted_at":"2026-11-05T13:24:14.032000+00:00","tools":["LLM","GitHub"],"seniority":"unknown","years_experience_min":null,"german_requirement":"preferred","german_level":"unspecified","english_requirement":"required","english_level":"unspecified","team_language":"unknown","confidence":0.9,"method":"rules","language_badge":"German a plus","language_evidence":"Languages: fluent in English, German is a plus","requirements_snippet":"Education: excellent Master's degree in Computer Science, Mathematics, Engineering, or a related field Experience and Knowledge: solid knowledge of computer science, artificial intelligence, machine learning, data science, or …","url":"https://jobs.smartrecruiters.com/BoschGroup/744000159866869-phd-neurosymbolic-reasoning-for-domain-adaptive-generative-ai","source":"smartrecruiters","agency":null,"description_text":"Job Description\nLarge Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks. However, their performance often degrades in highly specialized domains that require precise structural reasoning, consistency, and domain-specific knowledge. In electronic hardware design, existing LLMs struggle with the structural and semantic complexity of circuit descriptions. Hardware designs are represented as interconnected graphs, including netlists, schematics, and hierarchical design projects. These representations carry essential information that is lost when converted into purely sequential text.\n• As a part of our team, you will research novel methods for adapting generative AI systems to specialized domains by integrating structured symbolic knowledge, without relying on extensive model retraining.\n• Your investigation will focus on mechanisms that enable Large Language Models (LLMs) and other foundation models to leverage external knowledge representations such as knowledge graphs, ontologies, and formal design representations while preserving the reasoning capabilities of symbolic systems.\n• You will develop innovative approaches that enable generative AI models to interact directly with these structured representations, allowing for more precise reasoning, design assistance, verification, and code generation.\n• Through your work, you will make a key contribution to the development of next-generation AI systems that combine the flexibility of neural models with the reliability, interpretability, and structured reasoning capabilities of symbolic representations.\n\nQualifications\n• Education: excellent Master's degree in Computer Science, Mathematics, Engineering, or a related field\n• Experience and Knowledge: solid knowledge of computer science, artificial intelligence, machine learning, data science, or electrical engineering; experience with deep learning, graph algorithms, and knowledge representation; knowledge of hardware design is an advantage\n• Personality and Working Practice: you approach interdisciplinary research questions with curiosity and creativity, engage openly with others, communicate your ideas clearly, and actively seek out new challenges\n• Languages: fluent in English, German is a plus\n\nAdditional Information\nhttps://www.bosch-ai.com\n\nwww.bosch.com/research\nThe final PhD topic is subject to your university.\nStart: according to prior agreement\nPlease submit all relevant documents (CV, certificates, and links to GitHub or kaggle account).\nDiversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.\nNeed support during your application?\n\nCelina Dannecker (Human Resources)\n\n[entfernt]\nNeed further information about the job?\n\nMirjam Steger (Functional Department)\n\n[entfernt]\n\nWork #LikeABosch starts here: Apply now!\n\nCompany Description\nAt Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.\nThe Robert Bosch GmbH is looking forward to your application!","responsibilities_text":"Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks. However, their performance often degrades in highly specialized domains that require precise structural reasoning, consistency, and domain-specific knowledge. In electronic hardware design, existing LLMs struggle with the structural and semantic complexity of circuit descriptions. Hardware designs are represented as interconnected graphs, including netlists, schematics, and hierarchical design projects. These representations carry essential information that is lost when converted into purely sequential text.\n• As a part of our team, you will research novel methods for adapting generative AI systems to specialized domains by integrating structured symbolic knowledge, without relying on extensive model retraining.\n• Your investigation will focus on mechanisms that enable Large Language Models (LLMs) and other foundation models to leverage external knowledge representations such as knowledge graphs, ontologies, and formal design representations while preserving the reasoning capabilities of symbolic systems.\n• You will develop innovative approaches that enable generative AI models to interact directly with these structured representations, allowing for more precise reasoning, design assistance, verification, and code generation.\n• Through your work, you will make a key contribution to the development of next-generation AI systems that combine the flexibility of neural models with the reliability, interpretability, and structured reasoning capabilities of symbolic representations.","requirements_text":"• Education: excellent Master's degree in Computer Science, Mathematics, Engineering, or a related field\n• Experience and Knowledge: solid knowledge of computer science, artificial intelligence, machine learning, data science, or electrical engineering; experience with deep learning, graph algorithms, and knowledge representation; knowledge of hardware design is an advantage\n• Personality and Working Practice: you approach interdisciplinary research questions with curiosity and creativity, engage openly with others, communicate your ideas clearly, and actively seek out new challenges\n• Languages: fluent in English, German is a plus","status":"open","duplicate_of":null,"first_seen":"2026-11-05T16:15:23.608354+00:00","responsibilities_summary":null,"requirements_summary":null,"all_tools":["LLM","GitHub"],"posting_language":"en","evidence":[{"language":"german","requirement":"preferred","quote":"Languages: fluent in English, German is a plus","source":"rules"},{"language":"english","requirement":"required","quote":"Languages: fluent in English, German is a plus","source":"rules"}],"years_experience_max":null,"mobility_evidence":[],"model_version":"rules-9","english_badge":"English required","company_id":150}