Master Thesis: Strategic Research Taxonomy Generation and Knowledge Discovery

Technology, Data & Digital · Data, AI & Analytics · Data Science · Machine Learning

In short

Ericsson Research is seeking a Master's student to investigate strategic research taxonomy generation and knowledge discovery. The thesis will involve reviewing existing research, developing methods to combine predefined organizational domains with data-driven discovery, and evaluating the results to improve research discoverability and support strategic decision-making.

Responsibilities

Review research on taxonomy generation, ontology learning, topic modelling, knowledge graphs, scientific knowledge organisation, and research mapping.
Investigate existing Ericsson Research taxonomies, strategic research-area definitions, research artefacts, and previous work on automatic taxonomy generation and topic discovery.
Develop or adapt methods for incorporating predefined top-level research areas into a data-driven taxonomy-generation process.
Develop techniques for identifying subfields, relationships and overlaps between fields, emerging and interdisciplinary topics, and gaps between expected and observed research activity.
Evaluate the generated taxonomy using quantitative and qualitative methods.
Develop a prototype and demonstrate how the taxonomy can be integrated into Ericsson Research Insight Exploration to support navigation, discovery, and exploration of research knowledge.
Analyse the results and provide recommendations for long-term taxonomy management, knowledge discovery, and future integration.
Present the findings, evaluation results

Requirements

Pursuing a Master’s degree in Computer Science, Machine Learning, Data Science, Information Retrieval, Knowledge Engineering, Human–Computer Interaction, or a related field.
Strong analytical, research, problem-solving, and communication skills.
Interest in organisational knowledge management, research mapping, information retrieval, or AI-supported knowledge discovery.
Programming experience in Python.
Ability to work independently with an open research question.

Benefits

Outstanding opportunity to use skills and imagination to push boundaries.
Challenged but not alone, joining a team of diverse innovators.
Opportunity to craft what comes next.

Skills

PythonMachine LearningData ScienceInformation RetrievalKnowledge EngineeringHuman-Computer InteractionTopic ModellingKnowledge Graphs

#Research#Taxonomy Generation#Knowledge Discovery#Master Thesis#Stockholm

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