⛔ This position is no longer available.
University: RWTH Aachen University
Country: Germany
Deadline: 2026-05-04
Fields: Computer Science, Mathematics, Artificial Intelligence, Biomedical Engineering, Data Science
Are you passionate about advancing artificial intelligence to solve complex biomedical and life science challenges? Do you aspire to develop cutting-edge methods in causal inference and machine learning that can make a real-world impact? If so, a fully funded PhD position in Causal AI at RWTH Aachen University in Germany might be the ideal next step for your academic journey.
About the University or Research Institute
RWTH Aachen University, located in the historic city of Aachen, Germany, is one of Europe’s leading institutions for science and technology. Renowned for its strong emphasis on interdisciplinary research and innovation, RWTH Aachen consistently ranks among the top technical universities in Germany and Europe. The university’s vibrant academic environment attracts students and researchers from across the globe, fostering collaboration and the exchange of ideas. With state-of-the-art facilities and close partnerships with industry and research institutes, RWTH Aachen offers an outstanding setting for ambitious scholars seeking to push the boundaries of knowledge.
Germany itself is recognized for its commitment to research excellence, robust funding for higher education, and a high standard of living. International students and researchers benefit from a welcoming atmosphere, strong infrastructure, and numerous opportunities for professional and personal growth.
Research Topic and Significance
The focus of this PhD position is on Causal AI, specifically the development of robust frameworks for causal discovery and inference within the context of biomedical and life sciences. The research aims to address some of the most pressing challenges in modern data science, including the integration and modeling of large-scale multimodal biomedical datasets, uncertainty quantification, and the incorporation of expert knowledge.
Causal AI is at the forefront of artificial intelligence research, enabling machines to not only recognize patterns but also to understand cause-and-effect relationships. In the biomedical domain, this translates to more accurate diagnostics, better treatment recommendations, and deeper insights into complex biological processes. By tackling issues such as data heterogeneity, latent confounding, selection bias, and distributional shifts, this research has the potential to significantly advance both AI methodologies and their applications in healthcare and life sciences.
Project Details
The successful PhD candidate will join a dynamic research group led by Dr. Adèle Ribeiro, working at the intersection of causal discovery, Bayesian and probabilistic machine learning, and multimodal data modeling. The project will focus on developing new methods for causal abstraction and representation learning, with the ultimate goal of building frameworks that can robustly operate on diverse and complex biomedical datasets.
The position is fully funded for three years at 100% TV-L E13, providing a competitive salary and excellent resources for research and professional development. The group’s research is embedded within a stimulating environment that encourages collaboration, creativity, and the pursuit of high-impact scientific questions.
Candidate Profile
Ideal applicants will have a strong academic background in mathematics, computer science, or a closely related discipline. Experience in causal inference, machine learning, Bayesian statistics, or biomedical data analysis is highly desirable. Successful candidates should demonstrate:
– Motivation to contribute to causal and explainable AI for life sciences
– Solid analytical and problem-solving skills
– Interest in integrating expert knowledge and uncertainty quantification in AI models
– Ability to work independently and as part of a collaborative team
– A growth mindset and enthusiasm for tackling challenging research problems
Applicants should be eager to learn, contribute new ideas, and help advance the state of the art in causal AI.
Application Process
Applications should include:
– Curriculum Vitae (CV)
– Names and contact information of two references
– Research statement (up to two pages) outlining your background, research interests, previous contributions, motivation for joining the group, and your expectations and goals for the position. Please highlight up to three of your most significant publications and detail your contributions.
Please refer to the official advertisement for application details.
Review of applications begins on 2026-05-04. Early submission is strongly encouraged, but later applications may still be considered.
For more details and to find out how to apply, please visit the LinkedIn post:
https://www.linkedin.com/posts/ad%C3%A8le-ribeiro-80055817_i-am-looking-for-one-postdoctoral-researcher-share-7445134612588224512-VhjC
Conclusion
This is an exceptional opportunity for motivated researchers to contribute to the advancement of Causal AI in the biomedical and life sciences at one of Europe’s premier technical universities. If you are eager to make a meaningful impact and develop your expertise in a supportive and innovative environment, you are encouraged to apply. Stay connected for more opportunities like this in the future.
Questions & Answers
Question: What makes RWTH Aachen University a great choice for international PhD students?
RWTH Aachen offers a world-class research environment, strong international networks, and excellent support services for international students, making it an attractive destination for aspiring researchers.
Question: What is the main focus of the Causal AI research in this position?
The research centers on developing robust causal inference frameworks for large-scale multimodal biomedical data, addressing challenges like uncertainty, heterogeneity, and confounding.
Question: What background is ideal for applicants?
Candidates with strong foundations in mathematics, computer science, or related fields, and experience or interest in machine learning, causal inference, or biomedical data analysis are preferred.
Question: Is knowledge of German required for this position?
While the working language in many research groups at RWTH Aachen is English, knowledge of German can be helpful for daily life, but it is not a strict requirement for most international PhD positions.
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