PhD position - Data-driven Dynamic Reduced-Order Modeling for Energy Systems Optimization
Forschungszentrum Jülich

PhD position - Data-driven Dynamic Reduced-Order Modeling for Energy Systems Optimization

  • PhD / Postdoc / Thesis (From 25 to 36 months)
  • Jülich (Germany)
  • Published on August 26 2021
You want to apply your data science knowledge to the basic research questions and societal challenges of our modern world? Our scientists in HDS-LEE address some of the most pressing issues of our time, such as energy transition, climate change and resource scarcity, brain function, drug design, identification of diseases at very early stages.
As Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE), we aim to educate and train the next generation of data scientists during their doctoral thesis in close contact to domain-specific knowledge and research in three application domains: Life and medical science, earth science, energy systems and material science. Visit HDS-LEE at:

This HDS-LEE PhD position will be located at the Institute of Energy and Climate Research, Energy Systems Engineering (IEK-10) at Forschungszentrum Jülich. At IEK-10 we focus on the development of models and algorithms for simulation and optimization of decentralized, integrated energy systems. Such systems are characterized by high spatial and temporal variability of energy supply and demand as well as by a high degree of interdependence of material and energy flows. Research at IEK-10 aims to provide scalable and real-time capable methods and tools which enable the energy-optimal, cost-efficient and safe design and operation of future energy systems.

We are offering a

PhD position - Data-driven Dynamic Reduced-Order Modeling for Energy Systems Optimization

Your Job:

The efficient, ecological, and secure operation of energy systems with a high share of renewable electricity requires synchronized behavior of flexible energy producers and consumers. Consequently, optimal design and operation problems for energy systems need to consider the dynamic behavior of subordinate units leading to large optimization problems. To tackle these problems, models need to describe the relevant macro-scale behavior of a component for system-wide optimization problems. Increasingly, this need is being addressed by data-driven models due to their inherent flexibility. However, data-driven models are usually trained based on uniform prediction accuracy. Models trained in such fashion do not necessarily match the requirements for a specific deployment imposed by an expert modeler.

The scope of this project is to develop a method for training neural network-based dynamic reduced-order models in an integrated framework that allows (i) automated consideration of system requirements in the model training and (ii) optimal representation of the models for use in numerical methods.

Your tasks in detail

  • Develop a framework for data-driven dynamic reduced-order modeling of energy systems
  • Extend the framework to account for training criteria based on systems knowledge about the target applications of the reduced-order models
  • Integrate the framework into our in-house energy systems optimization environment COMANDO
  • Apply the new methodology and models to challenging design and operations problems
Your Profile:

  • High interest to apply your data science knowledge to energy science
  • Excellent Master`s degree in energy/process systems engineering, computational engineering science, simulation science, or a relevant discipline
  • Expert knowledge of at least one programming language (preferably Python)
  • Expertise in modeling, simulation and (applied) machine learning
  • Excellent skills in spoken and written English (TOEFL or equivalent evidence)
  • Excellent organizational skills and the ability to work independently
  • Excellent cooperation and communication skills and ability to work as part of a team
  • High level of scholarship as indicated by bachelor and master study transcripts and two reference letters

Our Offer:

This HDS-LEE PhD position will be located at IEK-10, Forschungszentrum Jülich. We offer:

  • Outstanding scientific and technical infrastructure - ideal conditions for successfully completing a doctoral degree
  • A highly motivated group as well as an international and interdisciplinary working environment at one of Europe's largest research establishments
  • Continuous scientific mentoring by your scientific advisors
  • Chance of participating in (international) conferences
  • Unique HDS-LEE graduate school program
  • Qualification that is highly welcome in industry
  • Targeted services for international employees, e.g. through our International Advisory Service
  • Further development of your personal strengths, e.g., via a comprehensive further training program - a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors:

Please upload the following documents with your application:

  • Application letter explaining the motivation for the position and research interests
  • Curriculum vitae
  • Bachelor and master or diploma study transcripts
  • English skills: TOEFL score or equivalent evidence of English-speaking skills (high school diploma "Abitur" with English as main school subject)
  • Two letters of recommendation/ reference letters (alternatively you can provide the name and contact details of two referees)

We offer you an exciting and varied role in an international and interdisciplinary working environment. The position is for a fixed term of 3 years. Your salary is in line with 100% of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund) and additionally 60 % of a monthly salary as special payment ("Christmas bonus"). Pay higher than the basic pay may be possible. Further information on doctoral degrees at Forschungszentrum Jülich including our other locations is available at:

Forschungszentrum Jülich promotes equal opportunities and diversity in its employment relations.

We also welcome applications from disabled persons.