PREDOCTORAL RESEARCHER POSITION IN AI TOOLS FOR THE OPERATIONAL INTEGRATION OF FLEXIBLE DISTRIBUTED ENERGY SYSTEMS

Tasks Description

• Review the state of the art on distributed energy systems, flexibility, balancing markets and applied AI to these systems.
• Define representative use cases, operational objectives, system constraints, and performance indicators.
• Integrate and preprocess data from distributed resources, weather services, electricity markets, grid operators, and simulation environments.
• Develop hybrid physical and data-driven models to predict or simulate operational states, storage conditions, and flexibility needs.
• Design and implement AI-based optimization and decision-support methods for interoperability in distributed real-world energy systems.
• Evaluate and validate the proposed hybrid methods through simulations, experimental platforms, or real-world case studies.
• Communicate and disseminate research results through high-quality scientific channels including high impact scientific publications, presentations in conferences.
• Supervision of Master or undergraduate studies

Requirements

Mandatory Requirements:

• Official Master’s degree in Artificial Intelligence, Machine Learning, Computer Science or related field, providing eligibility to undertake doctoral studies within the research line specified in this call.
• Bachelor’s degree in Artificial Intelligence, Machine Learning, Computer Science, Electrical, Electronic or Industrial Engineering or related engineering.
• Demonstrate experience of programming skills in Python and/or Matlab.

Other Merits To Value

• Academic record
• (International) mobility
• Solid understanding of applied AI, including modelling, prediction and optimisation techniques.
• Good understanding of renewable and energy storage technologies, planning and operation of power systems and energy and balancing markets.
• Good knowledge of DIgSILENT PowerFactory, SimPowerSystems and other energy systems modelling tools.
• Experience training and evaluating AI/ML models on real-world datasets (beyond coursework or toy problems).
• Experience working with imperfect, noisy, or heterogeneous datasets.
• Fluency in written and spoken English.
• Motivation for research and interest in applying AI to energy systems.
• Experience participating in research projects and interdisciplinary teams.