This internshipfocuses on developing and evaluating a token-efficient AI-powered knowledge hubthat provides a single point of access to operational information distributedacross collaboration platforms, documentation repositories, and tutorials. Inaddition to curating approved knowledge sources and configuring a prototypeagent, the intern will investigate how prompt design, context selection,retrieval strategies, knowledge summarisation, conversation-memory management,and response-length controls can minimise input and output token usage. Theseapproaches will be evaluated against answer accuracy, relevance, sourcetraceability, reliability for unsupported questions, latency, and computationalcost. A three-month placement will deliver a tested prototype, an initialtoken-efficiency evaluation, and a handover package; an extension to six monthswill support broader experimentation, a user pilot, feedback-drivenimprovements, and recommendations for future deployment. The work will providepractical research experience in AI-agent development, information retrieval,knowledge management, systematic evaluation, and responsible use of organisationalinformation.
Research topic:Token-efficient AI-agent design: investigate and evaluate methods forminimising input and output token usage while maintaining answer accuracy,relevance, source traceability, and reliable handling of unsupported questions.The work may compare prompt design, context selection, retrieval strategies,knowledge summarisation, conversation-memory management, and response-lengthcontrols, with the goal of identifying an effective balance betweenperformance, latency, and computational cost.
Type of internship: Master internship, Bachelor internship
Duration: 3-6 months
Required educational background: Computer Science, Electrotechnics/Electrical Engineering, Other, IT
Supervising scientist(s): For further information or for application, please contact Saeideh Alinezhad Chamazcoti (< correo electrónico eliminado por razones de seguridad >)
The reference code for this position is 2026-INT-298. Mention this reference code in your application.
Only for self-supporting students.
Applications should include the following information:
Incomplete applications will not be considered.
Research topic:Token-efficient AI-agent design: investigate and evaluate methods forminimising input and output token usage while maintaining answer accuracy,relevance, source traceability, and reliable handling of unsupported questions.The work may compare prompt design, context selection, retrieval strategies,knowledge summarisation, conversation-memory management, and response-lengthcontrols, with the goal of identifying an effective balance betweenperformance, latency, and computational cost.
Type of internship: Master internship, Bachelor internship
Duration: 3-6 months
Required educational background: Computer Science, Electrotechnics/Electrical Engineering, Other, IT
Supervising scientist(s): For further information or for application, please contact Saeideh Alinezhad Chamazcoti (< correo electrónico eliminado por razones de seguridad >)
The reference code for this position is 2026-INT-298. Mention this reference code in your application.
Only for self-supporting students.
Applications should include the following information:
- resume
- motivation
- current study
Incomplete applications will not be considered.
