Germany – PhD in Counterfactual Reasoning for Material Discovery at RWTH Aachen University

University: RWTH Aachen University

Country: Germany

Deadline: Not specified

Fields: Computer Science, Physics, Mathematics, Machine Learning, Materials Science

Overview

Are you passionate about leveraging cutting‑edge machine learning to drive material discovery and make a tangible impact on science and technology? If you are eager to contribute to the development of novel AI methods that can revolutionize how we understand and design new materials, this PhD position at one of Germany’s premier research centers may be the perfect next step in your academic journey.

About the University & Research Institute

RWTH Aachen University is one of Germany’s largest and most renowned technical universities, consistently ranked among the top institutions in Europe for engineering and the natural sciences. Located in the vibrant city of Aachen, the university is known for its interdisciplinary research environment and strong ties to industry and international research networks. The doctoral position is offered in collaboration with Forschungszentrum Jülich, one of Europe’s leading interdisciplinary research centers, which provides state‑of‑the‑art facilities and a stimulating scientific environment. The Helmholtz School for Data Science in Life, Earth and Energy (HDS‑LEE) graduate school, where this position is embedded, is dedicated to training the next generation of data scientists by fostering close collaborations between computational and domain‑specific research in life sciences, earth sciences, and energy/materials.

Research Topic and Significance

The core focus of this PhD project is to develop innovative machine learning methods—specifically, counterfactual reasoning approaches—for material discovery. Explainable Artificial Intelligence (XAI) has recently adopted counterfactual reasoning to enhance the interpretability of machine learning predictions by identifying the minimal changes required to alter model outcomes. In the context of materials science, this project will extend these ideas to answer a fundamental question: “What is the smallest, actionable change needed to transform material A into a version with property B?”

Project Details

The PhD Position Is Part Of The HDS‑LEE Graduate School And Will Be Located At Forschungszentrum Jülich. The Doctoral Degree Will Be Awarded By RWTH Aachen University. The Project Offers An Interdisciplinary Environment, Cutting‑edge Technical Infrastructure, And Ongoing Scientific Mentorship. Key Tasks Include:

• Reviewing existing literature and collecting relevant data to build a comprehensive knowledge and data repository.
• Designing and implementing effective material counterfactuals to propose interpretable, synthesis‑proximal modifications to known materials.
• Creating generative models for material discovery that adhere to strict physical constraints, ensuring the stability and synthesizability of new crystal structures.
• Presenting research findings at international conferences, engaging with the latest advances in machine learning, explainable AI, and generative AI for material sciences.
• Participating in the HDS‑LEE graduate school program and mentoring interns and student projects.

Additional benefits include 30 days of annual vacation and contributions to Germany’s comprehensive social insurance system.

Candidate Profile

The Ideal Candidate Will Have:

• An excellent Master’s degree in computer science, physics, mathematics, or a closely related field, with a focus on machine learning, deep learning, or AI.
• Strong mathematical, algorithmic, or physics background and distinct analytical skills.
• Advanced programming skills (Python, C++) and proficiency in computer platforms (Linux, Windows).
• Excellent cooperation and communication skills, and a passion for working in diverse, international, and interdisciplinary teams.
• Very good English language skills (spoken and written); German language skills are not required but are a plus.
• High motivation, strong work ethic, and a demonstrable passion for machine learning research, evidenced by prior research experience such as internships, study projects, or publications.

Applicants should be enthusiastic about making a societal impact through research and should be able to demonstrate their motivation and relevant research experience in their application materials.

Application Process

To Apply, Candidates Should Prepare The Following Documents:

• Statement of research interests
• Curriculum Vitae (CV)
• Copies of degrees and transcripts of records
• A copy of or a link to the Master’s thesis (or a draft thereof)
• Published articles or other relevant materials (e.g., letters of recommendation, if applicable)

For detailed instructions on the application process, tips, and FAQs, please consult the official job advertisement at

Conclusion

This is a unique opportunity to join a world‑class research community and contribute to groundbreaking advances in machine learning and materials science. If you are driven by curiosity, eager to work at the intersection of AI and real‑world applications, and looking to launch your research career in an internationally recognized environment, we encourage you to apply for this PhD position. For more opportunities and updates, follow the official channels of RWTH Aachen University and Forschungszentrum Jülich.

#J-18808-Ljbffr