IMDEA Energy is opening a Predoctoral Researcher Positionfor the Artificial Intelligence Unit, focused on applying advanced AI methods to complex energy systems. The position offers solid research training combined with real‑world applications, enabling career development in both academia and industry.

Your work will not be about automating corporate workflows with AI agents. Instead, you will apply advanced AI techniques to real, global, high‑impact problems that shape how energy systems operate and how society functions every day.

This PhD places you at the intersection of two fast‑growing fields, AI and Energy, providing strong long‑term career prospects. AI has the potential to transform the energy sector, and you will contribute to that transformation. Using methods such as reinforcement learning, optimization, generative AI, and causal AI, you will help model, simulate, and optimize systems addressing critical challenges including renewable integration, energy storage, grid stability, and energy access.

We are looking for candidates who already have a solid foundation in machine learning and are motivated to push the boundaries of applied AI in real-world, high-impact systems.

This project focuses on developing transferable and data-efficient building energy models, addressing one of the key challenges in applied AI: generalization across heterogeneous, real-world environments. The objective is to design models that can adapt across buildings, climates, and regions with limited or noisy data.

You will work in an internationally recognized research institute, with access to funding, infrastructure, and strong collaboration network. The environment combines research excellence with practical impact, with close guidance from the Head of AI to help you maximize both productivity and creativity, while building a sustainable and balanced professional career.

Join us to build real-world AI solutions for sustainable energy systems, powered by data, curiosity, and impact.

The requirements are as follows:

Tasks Description:

• Build a training corpus by integrating large public building energy datasets (e.g., ASHRAE Energy Prediction dataset, Building Data Genome Project, Buildings Bench), including feature harmonization and temporal alignment.
• Develop a strong source model for building energy consumption trained on data-rich regions.
• Design and implement transfer learning and few-shot adaptation pipelines for buildings and regions with limited or noisy data.
• Create an evaluation framework for cross-building and cross-country generalization.
• Validate the approach in real-world scenarios, including deployment on unseen regions, including under-developing countries, and integration with digital twins.
• Develop a generative‑AI interface enabling operators to query predictions and insights in natural language for decision‑support.
• Contribute to the development of digital twins for complex energy systems and infrastructures.
• Collaborate with researchers and industry partners in interdisciplinary projects.
• Participate in scientific dissemination: publications, conference, and project meetings.
• Take ownership of research challenges and contribute to publishable outcomes in a learn-by-doing environment with continuous mentoring

Compulsory Requirements:

• Official Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, Data Science, or a related field.
• Bachelor’s degree in Artificial Intelligence, Machine Learning, Computer Science, Data Science, or a related field.
• Strong programming skills in Python.

Other merits to value:

• Solid understanding of applied AI, including modeling, optimization and data processing techniques.
• Experience training and evaluating ML models on real-world datasets (beyond coursework or toy problems).
• Familiarity with time-series modeling, forecasting, or sequential data problems.
• Experience working with imperfect, noisy, or heterogeneous datasets.
• Ability to read, understand, and implement methods from recent scientific literature.
• Experience with simulation, digital twins, or decision-support systems.
• Familiarity with version control (Git) and good software engineering practices.
• Strong problem-solving skills and ability to work proactively and independently.
• Strong organizational and documentation skills.
• Fluency in written and spoken English.
• Motivation for research and interest in applying AI to energy systems.
• Interest in both academic and industry‑oriented career development.
• Experience participating in research projects or interdisciplinary teams.
• Knowledge of energy systems.

• Adequacy of the candidate's training to the requested profile.
• Work experience related to the profile of the job position.
• Knowledge of English.
• Motivation for accomplishing high-quality research & software systems.

• Remuneration: Between 22.000€ – 25.000€ per year, depending upon qualification and expertise.

*The contract includes all the benefits of the Spanish Public Health and Social Security System.

• Contract: Predoctoral contract of a maximum duration of four years.

IMDEA Energía, Móstoles, Madrid