PhD position - Data-Driven Distribution Models for Robust Design of Island Energy Systems with a High Share of Renewable Electricity
Forschungszentrum Jülich

PhD position - Data-Driven Distribution Models for Robust Design of Island Energy Systems with a High Share of Renewable Electricity

  • 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: https://www.hds-lee.de/

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 Distribution Models for Robust Design of Island Energy Systems with a High Share of Renewable Electricity

Your Job:

Optimization-based design of future energy systems is subject to uncertainties that stem from the intermittent and non-dispatchable renewable electricity sources. Fluctuation in local renewable electricity supply usually can be balanced via grid connection. However, some systems, e.g., island systems, require robustness against the uncertainty in their electricity sources. Robust optimization techniques allow to arrive at robust system designs but rely on accurate distribution models for the uncertain data at hand. In particular, distribution models for uncertain time series like renewable electricity generation remain a largely unexplored field in robust design.

The scope of this PhD project therefore is (i) to find efficient ways to model potentially correlated distributions using machine learning, particularly artificial neural networks (ANNs), and (ii) to utilize global optimization for robust sampling of the derived distribution models.

Your tasks in detail:

  • Advance preliminary work on neural network-based distribution models for energy time series
  • Combine machine learning with robust programming and global optimization
  • Extend our in-house framework for energy systems optimization by efficient formulations and algorithms for design and operation of energy systems with embedded ANNs
  • Apply the new method and algorithms to challenging design and operations problems


Your Profile:

  • You have a high interest to apply your data science knowledge to energy science
  • Excellent Master`s degree in computational engineering, energy/process systems engineering, simulation science, or a relevant discipline
  • Expert knowledge of at least one programming language (preferably Python and C++)
  • Expertise in numerical optimization and machine learning
  • Excellent skills in spoken and written English
  • 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:

The 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: https://www.fz-juelich.de/judocs

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) and additionally 60 % of a monthly salary as special payment ("Christmas bonus"). Further information on doctoral degrees at Forschungszentrum Jülich including our other locations is available at: https://www.fz-juelich.de/gp/Careers_Docs

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

We also welcome applications from disabled persons.