PhD: Energy-Efficient Agentic AI — Measure Energy Use

The Technical Faculty of IT and Design, Department of Computer Science, Section for Distributed, Emb; Aalborg; On-site; DKK 350,000 - 410,000; Full time

Benefits offered by this job

Access to energy‑measurement testbeds

Collaboration with Aalborg University,

Job summary

The Technical Faculty of IT and Design, Aalborg University, invites applications for a PhD stipend focused on measuring and reducing the energy cost of agentic AI. The position is three years with an open start and involves collaboration across computer science and business school.

You will run experiments, quantify energy per task, and develop evidence-based comparisons between agentic AI and traditional software, contributing to open-source benchmarks and policy-relevant findings.

Qualifications

• Master's degree required in CS/SE or related field.
• Strong interest in efficiency and sustainability of computing systems.
• Experience with one or more of LLMs, agent frameworks, energy or performance measurement is an advantage.

Responsibilities

• Define and conduct experiments to measure energy usage of agentic AI.
• Develop datasets and methods to compare energy cost of agentic AI vs deterministic software.
• Collaborate with social scientists to frame industrial relevance and dissemination of results.
• Publish in international venues and contribute to open-source measurement tooling.

Skills

Experimental methods

Energy measurement

Python programming

Linux experience

Education

Master's degree in computer science or software engineering

Tools

RAPL

Performance counters

GPU/CPU HPC infrastructure

Job description

The Technical Faculty of IT and Design, Aalborg University, invites applications for a PhD stipend focused on measuring and reducing the energy cost of agentic AI. The position is three years with an open start and involves collaboration across computer science and business school.

You will run experiments, quantify energy per task, and develop evidence-based comparisons between agentic AI and traditional software, contributing to open-source benchmarks and policy-relevant findings.