07 Postdoctoral Fellowship at Forschungszentrum Julich, Germany

• Master’s degree or PhD in Data Science or Bioinformatics with strong experience in software development (front-end and back-end) and research data management
• Proficiency in programming languages such as Python, HTML, and JavaScript
• Profound knowledge and experience with data management topics such as RO-Crates, ELNs, ontologies, data repositories, and FAIR data principles
• Experience in ontology development, controlled vocabularies, and data curation is advantageous
• Structured and analytical thinking, as well as a systematic, careful working method
• Willingness to collaborate with existing research units at Forschungszentrum Jülich, within the NFDI, and connected projects
• Excellent English skills (written and spoken); working knowledge of German is a plus

This postdoctoral position focuses on scenario development, as well as the modelling, simulation and analysis of coupled power and gas grids in Europe. The research project aims to develop advanced, cross-sectoral simulation frameworks that can be used to investigate the interaction between electricity, natural gas and hydrogen infrastructures under future energy transition scenarios, and to support informed decision-making for integrated energy systems.

• Master’s degree with subsequent PhD degree in physics, materials science or related fieldin Materials Sciences, Chemistry, Physics, Radiochemistry or a related discipline
• Experience in ceramics of solid state materials synthesis particularly involving difficult to handle or radioactive materials
• Interest in nuclear energy, waste management, nuclear fuel or spent nuclear fuel materials. Specific experience in spent nuclear fuel systems, actinide and radiochemistry is desirable
• Experience with microstructural analysis techniques such as X-ray diffraction and scanning electron microscopy including TEM, FIB, SEM and EDS techniques, is considered an advantage
• The ability to work in an international multidisciplinary team, and to work for extended periods of time in partner institutes abroad
• Excellent English communication and scientific writing skills (at least B2 level according to the CEFR), with a demonstrated track record of publishing in peer-reviewed journals

• Master’s degree with subsequent PhD degree in Nuclear Engineering, Computational Materials Science, Physics, Chemistry, or a closely related discipline
• Proven experience in mesoscale or engineering-scale modeling of nuclear fuel materials. Familiarity with physics-based rate theory, mechanistic modeling, or fuel performance codes such as SCIANTIX particularly but also TRANSURANUS or BISON is highly desirable
• Strong foundational knowledge in fission gas behavior (including diffusion, bubble nucleation, resolution, and microstructural evolution) and the mechanics of high burn-up spent nuclear fuel under extended interim storage conditions
• Solid scientific programming and data analysis skills (e.g., Python, C++ or Fortran) for script development, code execution, and parsing complex data sets
• Ability to independently design, execute, and troubleshoot your research output while aligning results with the overarching goals of the Atlas project
• Assist in the co-supervision and mentoring of PhD candidates, master’s students, or technical staff working within the research group whilst performing an active leadership role in the Atlas project
• Excellent English communication and scientific writing skills (at least B2 level according to the CEFR), with a demonstrated track record of publishing in peer-reviewed journals

At IET-1, we are studying the electrolysis of CO2 for the sustainable conversion of green energy into chemical energy carriers in order to defossilize the chemical industry. The process control and optimization of electrolysis from the cell to the stack requires automated monitoring, analysis, and control of the operating parameters and processes. As part of this project, high-throughput experimental systems will be enhanced using Design of Experiment (DoE) approaches in order to efficiently explore the high-dimensional parameter space of the electrolysis processes. DoE is essential for data-efficient exploration and optimization of the process parameter space, as well as for adaptive, data-driven machine learning approaches to map the electrolysis process to a digital twin. In parallel, data workflows and system control interfaces (application programming interface, API) are being developed to automate both process monitoring as well as process control. The API-based integration of the digital twin into the process control of the CO2 electrolysis enables autonomous operation of the system, which can differentiate between market-, system- or network-based operating modes depending on requirements.

• Doctoral degree in Physics, Computer Science, Mathematics, or a comparable field
• Proven research experience in quantum algorithms and/or quantum information theory
• In-depth knowledge of quantum algorithms (ideally Quantum Amplitude Estimation or related approaches, as well as oracle implementation)
• Solid understanding of quantum complexity-theoretic foundations (circuit complexity, oracle models, speedup guarantees)
• Experience in implementing quantum algorithms on real quantum computing systems or high-quality simulators
• Ability to work independently and in interdisciplinary teams
• Strong organizational and communication skills
• Ideally, experience in software development
• 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. Knowledge of the German language is not mandatory but certainly appreciated