Key Skills

Job Description

About This Position As wind and solar shares grow, more of the value of batteries, electrolysers and other flexible assets comes from short-term markets. Yet most energy system models represent only the day-ahead market, and therefore misprice flexibility. Intraday prices, where represented, are typically derived from historical correlations with day-ahead prices. Such approaches implicitly assume that price formation is independent of system composition. Because entering storage and flexibility shift the relationship between day-ahead and intraday prices, historically calibrated models misrepresent flexibility value precisely in the high-flexibility scenarios they are intended to evaluate. This internship applies conditional generative models, GANs, diffusion models or similar, to generate intraday price scenarios that respond to a future system configuration. The results feed into stochastic and rolling-horizon energy system optimisation. You will work with a multidisciplinary team at TNO at the interface of machine learning, electricity markets and energy system modelling. What will be your role? You will design, train and evaluate a conditional generative model for intraday prices or day-ahead to intraday spreads. Typical activities include Building a dataset from historical market and system data. Deriving conditioning features that describe system state and flexibility, using an energy system model (TulipaEnergyModel.jl). Implementing and training a conditional generative model, and comparing architectures. Benchmarking against simpler baselines such as quantile regression, copulas or historical resampling. Evaluating with proper scoring rules, and testing whether the model generalises to system configurations outside the training data. Translating findings into clear technical conclusions and recommendations. You will have space to bring your own ideas to the approach. What We Expect From You For this internship we are looking for a student with a background in Computer Science / Artificial Intelligence Data Science or Applied Mathematics Electrical Engineering Econometrics or Statistics Or a related field Preferred Qualifications Solid grounding in machine learning, in particular generative or probabilistic models. Strong Python skills; experience with a deep learning framework such as PyTorch is an advantage. Familiarity with time series modelling and probabilistic forecasting. Interest in electricity markets and energy systems; prior knowledge is not required. Ability to work independently and communicate technical results clearly. We are looking for a curious and proactive student who enjoys open-ended technical problems. 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. That is why we invest in an inspiring and inclusive working environment where colleagues can excel, have autonomy and continue to grow. 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. At TNO, we believe this is our time to help society, government and business move forward faster. Together with driven colleagues, you turn knowledge into concrete innovations or ventures that truly make a difference by combining the power of science and entrepreneurship. And in doing so, you make your mark on our time. The selection process You can apply for this internship position until the 13th of November, 2026. After submitting your application, you will receive a confirmation by email. TNO will arrange an appropriate internship agreement. If you have any questions about the content of the internship or the application procedure, you can contact the person listed below. If you start an internship with us, we will also ask you to provide a Certificate of Conduct (VOG). Important to know before applying Before the start of the internship, an internship agreement must be signed. For students enrolled at a Dutch university or university of applied sciences, TNO uses the UNL-template, supplemented with several TNO‑specific agreements. For students from foreign educational institutions and MBO (secondary vocational education) programmes, the TNO internship agreement applies. TNO does not sign any other internship agreements. Before the start of the internship, the educational institution will need to confirm in writing that you are enrolled at the educational institution for the duration of the internship, and; the internship forms part of your study programme. The confirmation of educational institution takes place by signing the UNL template or forms prepared by TNO. Interns at TNO must have a registered residential address in the Netherlands at the start of the internship. Carrying out internship activities from abroad is not possible. Are you an international student? Please note that you must have a BSN (Dutch citizen service number) before the start of your internship. Applying for a BSN can take several weeks, so make sure to start this process well in advance.

Core Responsibilities

Build a dataset from historical market and system data, develop conditioning features, and implement, train, and compare conditional generative models for intraday electricity prices or day-ahead-to-intraday spreads. Benchmark models against simpler approaches, evaluate their probabilistic performance and generalization to new system configurations, and communicate technical conclusions and recommendations.

Requirements

Applicants should be students in computer science, artificial intelligence, data science, applied mathematics, electrical engineering, econometrics, statistics, or a related field. They should have a solid foundation in machine learning, strong Python skills, and the ability to work independently and communicate technical results; experience with generative or probabilistic models, PyTorch, time series, and probabilistic forecasting is preferred.

Benefits

• Professional and innovative internship environment
• Personal supervision
• Professional development
• Laptop and work facilities
• Paid leave
• Travel expense contribution
• Jong TNO membership
• Networking opportunities
• Sports and social activities