MyTUM-Portal
Technische Universität München
18.03.2026, Wissenschaftliches Personal
We are looking for a PhD candidate to join the AI-Based Materials Science group in the Physics Department at the Technical University of Munich. In this position, you will work at the interface of artificial intelligence, materials science, and sustainable chemistry to develop new approaches for the recovery of rare earth elements.
Rare earth elements are essential for modern technologies, including renewable energy systems, electric vehicles, and digital electronics, yet their supply chains are fragile, and recycling rates remain low. Developing efficient and selective separation technologies is therefore a key challenge for the green and digital transition. In this project, you will apply state-of-the-art AI methods to optimize functionalized membranes for selective rare-earth recovery, helping accelerate the development of sustainable resource technologies. The project will be carried out in close collaboration with experimental partners in Singapore, who synthesize and characterize advanced membrane materials.
Your role and goals
Your research will focus on developing AI-driven strategies to optimize functionalized membranes for the selective recovery of rare earth elements. You will employ Bayesian optimization and other active learning techniques to guide experimental efforts by identifying optimal chemical compositions and processing conditions of membranes that maximize both selectivity and recovery efficiency.
Working closely with our experimental collaborators, you will analyse and model experimental data to efficiently explore the large design space of membrane functionalization and processing parameters. You will develop machine learning models to analyse experimental datasets and uncover structure-function relationships that determine membrane performance. By combining statistical learning, chemical intuition, and experimental feedback, you will help reveal the mechanistic origins of high-performance separation membranes and provide design rules for next-generation materials. The project will therefore combine AI-driven optimization, data-driven discovery, and close interaction with experiments.
You will work in a collaborative, interdisciplinary team and interact closely with experimental partners in Singapore, including the possibility of a research visit to gain hands-on insight into the membrane systems being studied.
Your experience and ambitions
We welcome candidates with a Master’s degree in physics, chemistry, materials science, chemical engineering, or a related field who are excited about applying machine learning and data science to real-world materials challenges. Experience in programming (in particular, Python) and an interest in machine learning, data analysis, or scientific computing are expected.
Prior experience with machine learning or optimization methods is beneficial but not strictly required. We know that no candidate will match every criterion from the start; if you bring a solid quantitative background, curiosity, and motivation to learn, we strongly encourage you to apply.
We seek curious researchers who enjoy working in a supportive, interdisciplinary team, are interested in sustainable technologies, and are eager to work at the intersection of AI, materials science, and experimental research.
What we offer
In the AI-Based Materials Science group, led by Prof. Patrick Rinke, we develop and apply modern machine learning methods for materials science, physics and chemistry. Our goal is to combine data-driven modelling, theory, and experiments to accelerate the discovery of materials for future technologies.
You will join a multi-cultural and cross-disciplinary research team and gain training in state-of-the-art AI methods such as Bayesian optimization, active learning, and scientific machine learning. The project involves close collaboration with experimental groups in Singapore, and a research visit to Singapore will be possible, offering the opportunity to work directly with our experimental partners and gain hands-on insight into the membrane systems being studied.
You will also become part of the broader materials science and AI ecosystem at TU Munich, including the e-conversion Excellence Cluster, the Munich Data Science Institute, the Munich Centre for Machine Learning, and the Atomistic Modelling Centre.
Our group values a respectful, inclusive work culture that supports teamwork, open communication, and work-life balance. You will receive close supervision and mentoring, as well as opportunities for professional development (e.g., conferences, workshops, and collaboration within our local and international networks). Where possible, we support flexible working arrangements in line with TUM regulations.
Munich provides a vibrant scientific environment at the crossroads of AI research, physics, materials science, and sustainable technologies. TUM is consistently ranked among the top universities in Germany and Europe, and Munich offers one of the highest qualities of living worldwide.
We particularly welcome female applicants and candidates who will broaden the diversity of our team. We are committed to creating an inclusive, supportive research environment in which all members can thrive.
Ready to apply?
If you want to join our community, please email your application to Prof. Patrick Rinke at Rinke.officenat.tum.de . The deadline for applications is April 2, 2026. The application material should include:
· . CV
· . Degree certificates and academic transcripts
· . A short statement of motivation describing your research interests
· . Contact details of at least two referees (or letters of recommendation, if already available)
The position will be filled as soon as a suitable candidate is identified. For additional information, kindly contact Prof. Patrick Rinke. TU Munich reserves the right for justified reasons to leave the position open, extend the application period, reopen the application process, and consider candidates who have not submitted applications during the application period.
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Kontakt: Rinke.officenat.tum.de
