Emerging Scholars Council

Several fully funded PhD / Post-Doctoral Positions (m/f/x)

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Saarland

Higher Education

Arbeitsmodell

Hybrid

vor 3 Tagen

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Saarland University

Several fully funded PhD / Post-Doctoral Positions (m/f/x)

Saarbrücken, Germany

2026-08-15 (Europe/Berlin)

Apply now

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Saarland University is an internationally recognized campus university with particular strengths in computer science, artificial intelligence and the materials and life sciences. With over 17,000 students and more than 4,000 staff, it is one of the largest employers in the region and an officially recognized family-friendly university.

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

Several fully funded PhD / Post-Doctoral Positions (m/f/x) Foundation Models and Agentic AI for Physical Systems Reference number W2878 (PhD) and W287G (PostDoc) , salary in accordance with the German TV-L salary scale1, pay grade E13 TV-L , 100% of standard working time , duration: 3 years for PhD positions, 3 years for PostDocs (with the option of extension).

As a point of reference: E13 TV-L starts (if no relevant prior experience) at €57.112 gross per year, not including annual special payments.

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 as well as the Saarland Informatics Campus with close ties also to other research institutes, thus offering access to one of Europe's leading AI ecosystem, with modern compute infrastructure.

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 optimisation 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 materials

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, or, as a PostDoc, develop your own research line) within one of the research directions above or a 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 the Hamburg University of Technology, and, as a PostDoc, help shape future research initiatives and funding proposals
Support teaching, co-supervise students (PhD/Master/Bachelor, as appropriate to your level) and collaborate actively across Saarland University, DFKI, the Saarland Informatics Campus, and international partners

Your profile

For PhD positions: above-average Master's degree (or equivalent) in Computer Science, Computational Engineering, Materials Science, Mechanical Engineering, Physics, Applied Mathematics or a closely related discipline
For PostDoc positions: completed doctoral degree (PhD / Dr.-Ing. / Dr. rer. nat.) in one of the above disciplines, with a demonstrated research record at the interface of AI and science
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 (according to CEFR: C1); 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 working remotely (part of the time)
Secure and future-oriented employment with attractive conditions
A broad range of further education and professional development programs (for example language courses)
An occupational health management model with numerous attractive options, such as our university sports program
Supplementary pension scheme (RZVK)
Discounted tickets on local public transport services ('Job-Ticket' of the saarVV)
Job bike leasing (JobRad)

How To Apply Please send your application as a single PDF (max. 10 MB) with the level and reference number you are applying for in the subject line (W2878 - PhD or W287G -- PostDoc) by August 15th 2026 to roland.aydin@uni-saarland.de.

Please include

A short motivation letter (1 page for PhD, up to 2 pages for PostDoc) stating which direction interests you most -- or proposing your own
A CV including publication list (PostDocs: please highlight your three most relevant papers)
Master's and Bachelor's transcripts (PhD applicants); PhD certificate or confirmation of submission (PostDoc applicants)
Names and contact details of two academic referees
Optional: links to GitHub, personal website, or preprints

Contact: Prof. Dr. Roland Aydin, Saarland University -- roland.aydin@uni-saarland.de

Pay-grade classification depends on the details of the position and the applicant's qualifications under the TV-L scale; part-time employment is generally possible. Foreign degrees require an equivalence statement by the Zentralstelle für ausländisches Bildungswesen (ZAB) before hiring (see https://www.kmk.org/zeugnisbewertung); interview and ZAB costs cannot be reimbursed. We welcome applications regardless of gender, nationality, ethnic and social origin, religion/belief, disability, age, and sexual orientation and identity. In accordance with its policy of increasing the proportion of women, the University actively encourages applications from women; applications from severely disabled persons will be given preferential consideration in the event of equal suitability. By submitting your application you confirm that you have taken note of the Saarland University privacy notice (Art. 13 DSGVO).

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Job details

Title

Several fully funded PhD / Post-Doctoral Positions (m/f/x)

Employer

Saarland University

Location

Saarland University Saarbrücken, Germany

Published

2026-07-07

Application deadline

2026-08-15 23:59 (Europe/Berlin)

2026-08-15 23:59 (CET)

Job type

PhD, Postdoc

Field

Materials Engineering, , Computational Engineering, , Computational Physics, , Materials Physics, , Artificial Intelligence, and 10 more Computational Sciences, Machine Learning, Mechanical Engineering, Applied Physics, Applied Mathematics, Programming Languages, Information Systems (Business Informatics), Software Engineering, Artificial Neural Network, Image Processing

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About The Employer Saarland University

Saarland University is a modern university within the Saar-Lor-Lux region -- a dynamic part of Europe defined by the shared borders between Germany,...

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