Jobid: ad6dc130aca8 (0.0497)
What will be your role?
As AI systems become increasingly embedded in diverse domains—from healthcare to finance—the need for transparent evaluation of their sustainability grows. Most AI research to date primarily benchmarks performance (accuracy, F1 score) at a broad task or system level, frequently ignoring the real energy impact of deploying such models for highly specific or granular workflows.
For example, while general speech-to-text benchmarks are widespread, little attention is paid to the energy and performance variance when models are used for domain-specific sub-tasks (e.g., transcribing clinical terminology in operating rooms versus everyday speech). In practice, optimizing for these specific tasks—including fine-grained model selection and adaptive deployment—can lead to significant reductions in resource usage, and thus, emissions.
At TNO, sustainability of AI systems is part of our mission to make AI systems more responsible. We seek to investigate new methods that systemically measure and compare both performance and energy consumption of models when executed on narrowly-defined tasks. By correlating accuracy with energy usage at the task-level, we aim to develop actionable guidelines and tools for practitioners to make environmentally conscious decisions in model selection and deployment.
Examples of fitting research questions:
What will you be doing?
As a graduation project candidate, you will design and implement experimental setups for energy measurement of AI models at the task level. You will explore literature on existing model benchmarking, energy profiling, and sustainability metrics. By developing tools and protocols, you will run controlled experiments on various tasks (such as entity extraction, domain-specific translation, or anomaly detection) and document both performance and energy data. You will have the opportunity to analyze and compare results across architectures, model sizes (LLMs, SLMs, classifiers) and deployment scenarios. Ultimately, your work will contribute to TNO’s suite of sustainable software and AI best practices. You will be supervised by experts in the Green IT and Data Science groups.
What we expect from you
We are looking for a student, who is interested in the field of (generative) AI and software engineering, who is interested in research and enjoys tackling technically challenging problems. The student should be able to work unsupervised and have a structured approach to accomplish their goals in their research proposal. Furthermore, the student should be comfortable working in a team and be willing to learn more about the domain and the field.
You are following a master’s study in a relevant field, such as artificial intelligence or computer science, etc. You possess good programming skills. You are currently studying within the Netherlands or other EU country and be willing to relocate to the Netherlands. You have good communication skills in English.
What you’ll get in return
You want an internship opportunity on the precursor of your career; an internship gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Furthermore, we provide:
Deel deze vacature: