Internship | Machine Learning for Smart Gas Flow Metering in Future Energy Systems

Make your mark on our time. Become an intern at TNO!

About this position

The European Commission aims to achieve a carbon-neutral energy system by 2050, requiring a transition from natural gas to renewable energy gases such as biomethane and hydrogen. This transition introduces new challenges for gas transmission and distribution networks, including increased variability in gas composition, supply and demand, flow rates, and a growing number of grid entry points.

These developments place higher demands on gas flow measurement. Current fiscal metering practices are estimated to underestimate measurement uncertainty by approximately 35%, while accurately quantifying flow meter uncertainty remains essential for metrological traceability, gas allocation, and billing. Consequently, there is a growing need for reliable models that describe gas network dynamics using high-quality measurement data.

To address these challenges, Distribution System Operators (DSOs) and Transmission System Operators (TSOs) are increasingly exploring Artificial Intelligence (AI) and Machine Learning (ML) to enhance gas grid monitoring, operation, safety, and flow metering. Developing trustworthy ML-based solutions requires accurate experimental data, robust uncertainty analysis, and models that are transparent, explainable, and compatible with existing metrological standards.

In this graduation project you will investigate the application of Machine Learning for smart gas flow metering and monitoring. The research will use experimental laboratory data and synthetic data generated through Computational Fluid Dynamics (CFD) simulations to develop and evaluate ML models. Depending on the student's interests, the project may focus on different modeling paradigm (e.g. classical ML, Physics-Informed Neural Networks (PINNs), Hybrid models, or Probabilistic ML).

What will be your role?

• Train and validate ML models using experimental and CFD datasets.
• Evaluate model performance and generalization.
• Quantify prediction uncertainty.
• Assess model explainability and transparency.
• Investigate compatibility with metrological standards such as ISO/IEC 17025, OIML R140, ISO 15112, and ISO/IEC Guide 98.

The selected student will be supervised by experts in Machine Learning and Energy System Modelling and will work closely with the Heat Transfer & Fluid Dynamics (HTFD) research group. The project provides access to high-quality experimental datasets and Computational Fluid Dynamics (CFD) simulations, offering hands-on experience in developing advanced data-driven models. This research contributes to the digitalization, reliability, and metrological accuracy of future gas energy systems, addressing challenges associated with the integration of renewable gases.

What we expect from you

• MSc student with experience in machine learning.
• Knowledge of fluid dynamics is a plus.
• You can work independently on different aspects of scientific research, including literature review and academic writing.
• You are collaborative and proactive.
• You are available to work on location in Rijswijk.

What you'll get in return

An internship at TNO means working in an environment where substance and impact are central. You will become part of a knowledge organisation where research and practice come together, and where experts collaborate on solutions to current societal and technological challenges.

Your internship is a period in which you can discover what suits you, where your strengths lie and what you would like to learn next. You are part of a professional working environment, gain insight into how things work in practice, and have the opportunity to build experience that goes beyond this internship alone. For many students, an internship is therefore also a first step in discovering whether TNO could be a potential next step after graduation.

In addition, we offer you:

• A professional and innovative internship environment in which you actively contribute to societal and technological challenges, working alongside leading experts in your field.
• Personal and dedicated supervision, with focus on your learning objectives, development and study assignment.
• Room to develop: you gain relevant work experience, develop both your subject-specific and professional skills, and build a valuable network.
• Use of a laptop and the facilities you need to perform your work effectively.
• A monthly internship allowance of € 615 for a full-time internship, for MSc, BSc and vocational education (MBO) students.
• Up to eight hours of leave per internship month for a full-time internship, allowing you to balance your internship with your studies and personal life.
• A contribution towards travel expenses if you are not entitled to a student travel card.
• A free membership to Jong TNO: the network for young colleagues, where you can meet other TNO colleagues and participate in sports activities, professional and personal development activities, and social events such as the annual ski trip.

TNO as an employer

Our people are at the heart of TNO. Their curiosity, expertise and entrepreneurial mindset make it possible to deliver high-impact research and innovations that contribute to society’s sustainable wellbeing and prosperity.

Your talent and ambition have every opportunity to flourish at TNO. You work with experts (both within and beyond TNO), have access to advanced technology and the freedom to explore, experiment and innovate. Our strength lies in independence, reliability and collaboration. We find each other in wonder and ingenuity. We are driven to push boundaries. By working with businesses and government, and by connecting different perspectives, we strengthen our innovative capability and create responsible, meaningful results.