Saarland University is a campus university with an international reputation for research excellence, particularly incomputer science and in the life sciences and nanosciences. The university is also distinguished by its close ties to France and its strong European focus. Around 17,000 students, studying over one hundred different academic disciplines, are currently enrolled at Saarland University. Saarland University is officially recognized as one of Germany’s family-friendly higher-education institutions and with a combined workforce of more than 4000 it is one of the largest employers in the region.

The newly established Chair of Data-Driven Simulation and Analysis in Materials Science (Prof. Dr. Roland Aydin) atSaarland University jointly affiliated with the German Research Center for Artificial Intelligence (DFKI), is inviting applications for the following position commencing at the earliest opportunity.

**Doctoral Research Position (m/f/x)**

Reference number **W2878**, salary in accordance with the German TV-L salary scale1, pay grade: E13 TV- L, duration of employment: 3 years, volume of employment: 100 % of standard working time.Workplace/Department: Saarland University, Chair of Data-Driven Simulation and Analysis in Material Science, Subject Area: FoundationModels and Agentic AI for Physical Systems.

**Job requirements and responsibilities:**

What we work on

Our group develops LLMs and agentic AI systems for scientific discovery, engineering and physical systems. We investigate how AI can reason about scientific problems, interact with simulation software and support the design of complex physical systems. The group is jointly embedded at Saarland University and DFKI in Saarbrücken.

**Current Research Directions include:**

- AI for scientific computing – neural operators and learning-based surrogates for physical systems
- LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows
- AI for engineering design – LLM-driven agents for autonomous optimization and design of materials, structures and processes
- Multimodal scientific AI – integrating language, images, sensor data and simulations
- Alignment for scientific AI – scalable oversight, verification and control of LLM-based agents
- AI for materials discovery – generative models, active learning and autonomous discovery of novel material

We welcome applications from candidates whose interests align with one or more of these directions and who are excited about advancing the interface between AI and the natural sciences.

**Your potential responsibilities**

- Pursue an independent research project (toward your doctorate) within one of the research directions above or closely related area
- Develop, train and evaluate modern ML models on GPU/HPC infrastructure – including graph neural networks,transformers, neural operators, diffusion models and foundation-model-based systems – and, where relevant, combine them with numerical simulation and scientific computing workflows,
- Publish at leading venues (e.g. NeurIPS, ICML, ICLR, AAAI, npj Computational Materials) and present your work at international conferences
- Contribute to collaborative research projects with our close partners at EPFL and at Hamburg University ofTechnology
- Support teaching, co-supervise students (Master/Bachelor, as appropriate to your level) an d collaborate actively across Saarland University, DFKI, Saarland Informatics Campus, and international partners

**Your academic qualifications:**

- Completed scientific university studies in Computer Science, Computational Engineering, Materials Science, Mechanical Engineering, Physics, Applied Mathematics or a closely related discipline (Above average Master’sdegree or equivalent)
- Language skills (according to GER): English C1

**The successful candidate will also be expected to have the following competences:**

- Solid foundations in machine learning and deep learning, together with an interest in scientific computing
- numerical simulation, computational engineering or computational materials science
- Strong programming skills in Python, both with vibe coding and without, and experience with PyTorch (or
- JAX/TensorFlow) and the scientific-Python ecosystem
- Independent and structured working style, strong communication skills and genuine enthusiasm for
- interdisciplinary research
- Excellent English skills for research, scientific writing and collaboration; German is not required at the time of hiring
- Welcome additions: publications, open-source contributions, or hands-on experience with: diffusion models, foundation models, LLM fine-tuning, agentic AI systems, or scientific simulation software

**What we can offer you:**

- A flexible work schedule allowing you to balance work and family, among other things the possibility of
- teleworking
- Secure and future-oriented employment with attractive conditions
- A broad range of further education and professional development programmes (for example language courses)
- An occupational health management model with numerous attractive options, such as our university sports programme
- Supplementary pension scheme (RZVK)
- Discounted tickets on local public transport services (‘Job-Ticket‘ of the saarVV)
- Job bike leasing (JobRad)

We look forward to receiving your meaningful online application (in a single PDF file, max. 10 MB) by** 15.08.2026** to roland.aydin@uni-saarland.de. Please include the reference number W2878 in the subject line of the e-mail.

**Please include:**

- A short motivation letter of 1 page stating which direction interests you most – or proposing your own
- A CV including publication list
- Master’s and Bachelor’s transcripts
- Names and contact details of two academic referees
- Optional: links to GitHub, personal website, or preprints

If you have any questions, please contact us for assistance.

Your contact:

Herr Prof. Dr. Roland Aydin, Chair of Data-Driven Simulation and Analysis in Materials Science, Saarland University

roland.aydin@uni-saarland.de

Frau Susanne Kern-Schumacher

Tel.: +49 681 302 70500