Postdoc (2 × 2-year appointments) Scientific Foundation Models for Materials (AI/HPC)

Are you excited about using large-scale AI to accelerate scientific discovery? Join a Horizon Europe project developing next-generation scientific foundation models that combine knowledge graphs, multi-modal data, and GPU-accelerated machine learning for materials science.

Information
We are seeking two highly motivated postdoctoral researchers to join the Horizon Europe project SIMU-LINGUA, a major European initiative developing scientific foundation models (SciFMs) for materials science.

SciFMs are emerging as a powerful paradigm for scientific discovery. SIMU-LINGUA addresses key challenges in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and trustworthy AI for science.

At Eindhoven University of Technology (TU/e), you will contribute to the project’s core technical components:

• scientific data orchestration and knowledge graphs
• architecture and large-scale training of scientific foundation models

You will collaborate with researchers across machine learning, scientific computing, materials science, and data engineering, and work with leading academic and industrial partners across Europe. The project will develop materials ontologies, training-ready datasets, multi-modal knowledge graphs, and large-scale SciFM models, together with tools for training diagnostics and model observability. The first pre-trained SciFM models will be released as part of the project. You will have significant scientific ownership, contribute to publications and open-source software, and help shape emerging methodologies for scientific AI and foundation models. Applicants should indicate a primary research track, although collaboration between tracks is expected.

Track A — Scientific Data & Knowledge Graphs
You will develop scalable scientific data infrastructures enabling large-scale model training, including materials ontologies, data ingestion and curation pipelines, multi-modal knowledge graphs, and training-ready datasets with robust provenance and validation.

Track B — GPU-Accelerated Scientific Foundation Models
You will design and train large-scale multi-modal foundation models, including SciFM architectures coupled to knowledge graphs, GPU-accelerated PyTorch training pipelines, distributed training on HPC systems, and tools for training diagnostics and observability, potentially integrating physics-aware constraints and generative modelling approaches.

Both tracks interact closely to create a data–model feedback loop, enabling systematic analysis of how scientific data, model architectures, and training dynamics influence scientific predictions. You will join a vibrant research environment at TU/e at the intersection of AI, scientific computing, and computational science, collaborating with leading European research groups and benefiting from advanced GPU and HPC infrastructure.

We are looking for researchers excited to work at the intersection of machine learning, scientific computing, and large-scale scientific data. You have:

• A PhD in Computer Science, Machine Learning, Applied Mathematics, Scientific Computing, Data Engineering, or a closely related field.
• Demonstrated ability to conduct high-quality academic research, reflected in publications or other research outputs.
• Strong programming skills in Python and experience with scientific computing environments.
• Experience in one or more of the following areas:
  
  • machine learning or deep learning (e.g. PyTorch)
  • scientific data pipelines or large datasets
  • knowledge graphs or structured data systems
  • GPU or distributed computing
  • scientific machine learning or physics-informed ML
• Experience working with Linux and HPC environments is an advantage.
• Strong communication skills and excellent proficiency in English.

You enjoy working in an international, interdisciplinary team, mentoring students, and contributing to ambitious collaborative research projects.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for 1 year with an extension of another year after a positive evaluation.
• Salary in accordance with the Collective Labour Agreement for Dutch Universities, scale 10 (min. € 4,241 max. € 5,538).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs on general skills, didactics and topics related to research and valorization.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate, for you and, if applicable, your partner.
• Partially paid parental leave and an allowance for commuting, working from home and internet costs.
• A TU/e Postdoc Association that helps you to build a stronger and broader academic and personal network, and offers tailored support, training and workshops.
• A Staff Immigration Team is available for international candidates, as are a tax compensation scheme (the 30% facility) and a compensation for moving expenses.

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Prof. V. Dolean-Maini, v.dolean.maini@tue.nl.

Visit our website for more information about the application process or the conditions of employment. You can also contact HR services M&CS, hrservices.mcs@tue.nl.

Are you inspired and would like to know more about working at TU/e? Please visit our career page.