This role centers on leveraging advanced mathematical and graph-theoretical expertise to enhance complex graph-based knowledge representations within the Knowledge Plane. You will analyze the structure and behavior of large-scale knowledge graphs, focusing on telecom domain structures and system contexts. The objective is to identify patterns, dependencies, and emergent properties, thereby refining these representations for superior reasoning and decision support.

As a Mathematical Graph Theory Specialist, you will apply deep mathematical reasoning to structured graph knowledge. Your contributions will provide graph-theoretical insights crucial for autonomous decision-making and the evolution of autonomous networks. This position is integral to advancing AI and data science capabilities in telecom analytics.

Interpret and analyze large-scale graph-based knowledge representations using advanced mathematical and graph-theoretical methods to uncover patterns and anomalies. Apply deep mathematical graph theory to deliver structural reasoning, explaining behaviors and guiding the refinement of knowledge representations. Represent information as structured graph knowledge through abstractions, ontologies, and semantic modeling. Reason about state, constraints, and relationships across telecom and system domains using various graph concepts. Drive the evolution of the Knowledge Plane to enable Autonomous Network capabilities. Embed graph-theoretical reasoning into analysis and decision-making workflows with TRAM Entities. Contribute graph-based reasoning into the IAADE loop to bolster autonomous decision-making. Advance mathematical graph methodologies with novel structural, spectral, probabilistic, or combinatorial approaches.

A strong foundation in advanced and applied mathematical graph theory, including structural, spectral, probabilistic, and combinatorial concepts, is essential. Demonstrated applied experience with graph-theoretical reasoning and structural analysis, such as flows, matchings, and embeddings, is required. Ability to analyze and improve graph-based knowledge representations with mathematically grounded insights is crucial. Experience with knowledge-graph and semantic technologies, including ontology design and semantic modeling, is necessary. Strong mathematical reasoning skills are needed to derive explanations for structural behaviors. Proficiency in Python, Java, and/or Scala, especially with graph-related frameworks, is a must. Experience applying graph-theoretical analysis in telecom or other complex system domains is highly valued. Familiarity with GQL or SPARQL query concepts is beneficial. Understanding of how graph-based reasoning supports autonomous decision-making frameworks is expected. A formal academic background in mathematics or a closely related field, with exposure to graph theory or combinatorics, is preferred.