At the Institute of Climate and Energy Systems - Energy Systems Engineering (ICE-1), we develop advanced models, algorithms, and control solutions for simulating, optimizing, and operating future integrated energy systems. We address the challenges arising from the increasing integration of renewable energy, sector coupling, decentralization, and growing energy infrastructure complexity. The Control Solutions department develops methods and algorithms for controlling energy systems and their components. We focus on application-oriented methods that can operate in real time and are suitable for operating decentralized systems. We aim to enable the reliable, cost-efficient, resilient, and sustainable operation of future energy networks by combining our knowledge of energy systems with cutting-edge developments in machine learning, generative AI, and digital infrastructures.
We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with:
- Team & Environment: You will work in a motivated team with an international and interdisciplinary focus – at one of the largest research institutions in Europe
- Research & Infrastructure: You will have access to excellent scientific and technical facilities for your work
- Networking & Exchange: You will participate in (international) conferences and project meetings and actively build your scientific network
- Supervision & Support: We will accompany your doctoral studies with continuous, expert guidance from your academic supervisor
- Work-life balance: We offer flexible working hours to help you balance your professional and personal life. You also have the option of flexible working (in terms of location), which is generally possible after consultation and in line with upcoming tasks and (on-site) appointments
- Annual Leave: You get 30 days of annual leave
- Knowledge & Development: Your professional development is important to us – we support you specifically and individually e.g., through training and networking opportunities specifically for doctoral candidates (JuDocS)
- Health & well-being: Your health is important to us. You can look forward to a comprehensive company health management programme with a wide range of options, including a beach volleyball court, running groups, yoga classes and much more. In addition, our company medical service and an experienced social counselling team are available to assist you on site
- Fair remuneration: Depending on your qualifications and assigned responsibilities, you will be classified according to pay group 13 (75%) of the TVöD-Bund. In addition to the basic salary, there is an additional year-end bonus under the collective pay agreement amounting to 75% of a monthly salary, as well as capital-forming benefits. All information about the TVöD-Bund collective agreement can be found on the BMI website (pay scale table on page 66 ff. of the PDF download)
- Perspective: The position is initially offered for a fixed term of 3.5 years, with the first six months serving as an orientation and probation period
- Support for international employees: Our International Advisory Service makes it easier for international employees to get started In addition to exciting tasks and a collegial working environment, we offer you much more: https://go.fzj.de/benefits
The increasing digitalization of modern energy systems, together with the large-scale integration of renewable energy sources and distributed energy resources, is generating unprecedented volumes of heterogeneous operational data. However, limited availability, privacy constraints, and the variable quality of real-world datasets continue to hinder the development of reliable AI solutions for energy system monitoring and control. In this context, generative AI and foundation models offer promising approaches for developing trustworthy and scalable energy applications, including the generation of synthetic yet physically realistic datasets for AI training, benchmarking, and validation. The successful candidate will contribute to AI.Grids, a flagship European initiative on the application of Artificial Intelligence for future power systems, involving leading research institutions as well as transmission system operators (TSOs) and distribution system operators (DSOs). The project aims to accelerate the development of trustworthy, scalable, and interoperable AI solutions for the operation and management of modern electricity networks. We are currently seeking a Ph.D. student to research the following topics:
- Development of generative AI and foundation model approaches for smart grids and energy systems
- Generation of synthetic yet realistic energy datasets based on real operational data from the FZJ campus and Living Lab Energy Campus (LLEC).
- Development of physics-informed and graph-based machine learning methods for energy system monitoring, forecasting, and planning
- Data analysis considering uncertainties, missing data, interoperability, privacy, and data governance aspects
- Development of scalable AI workflows for training, validation, and benchmarking using HPC and federated learning infrastructures
- Real-time validation and integration of developed methods into practical monitoring and control applications.
- Collaboration with European research and industrial partners within the AI.Grids ecosystem, including TSOs and DSOs, for the development of practical AI solutions for power system applications
- Excellent Master’s degree in Electrical Engineering, Computer Science, Physics, or a related field
- Strong background in mathematics, machine learning, and statistical modelling
- Excellent programming skills (e.g., Python, Matlab, C/C++) and experience with AI/ML frameworks
- Knowledge of data analysis, uncertainty quantification, and statistical methods for real-world datasets
- Interest in Generative AI, machine learning, and data-driven methods for energy systems and smart grids
- Familiarity with power systems, energy informatics, or digital energy infrastructures is an advantage
- Experience with synthetic data generation, graph neural networks, foundation models, or HPC environments is welcome
- Experience with real-time simulations, digital twins, or hardware-in-the-loop (HiL) validation is considered a plus
- Strong motivation for interdisciplinary and collaborative research in an international environment
- Very good command of written and spoken English with extensive vocabulary is required (at least B2 level according to the CEFR), ideally supported by a certificate confirming the language level. In addition, your business fluent German skills complete your profile (Prior German knowledge is not required)
We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us. The following links provide further information on diversity and equal opportunities: https://go.fzj.de/equality and on specific support options: https://go.fzj.de/womens-job-journey
