PhD – Neurosymbolisches Schlussfolgern für domänenadaptives generatives KI-System (w/m/div.)
Core
Researching novel methods to adapt generative AI systems to specialized domains by integrating structured symbolic knowledge without extensive retraining.
Role type
PhD researcher (Neurosymbolic reasoning for domain-adaptive generative AI)
Builds
Next-generation AI systems combining neural flexibility with symbolic reliability for hardware design verification and code generation.
Domain
Artificial Intelligence / Hardware Design / Knowledge Graphs
Deliverable
research
Required skills
Deep Learning, Graph Algorithms, Knowledge Representation, Machine Learning, Data Science
Preferred skills
Hardware Design, Formal Design Representations
Technologies
Large Language Models (LLMs), Foundation Models, Ontologies, Formal Design Representations
Responsibilities
Develop innovative approaches for generative AI models to interact directly with structured representations; Investigate mechanisms enabling LLMs to utilize external knowledge representations while retaining symbolic reasoning capabilities; Contribute to the development of AI systems that bridge neural and symbolic reasoning.
Seniority
PhD Candidate / Researcher
