The Technical University of Munich (TUM) and the Institute for Explainable Machine Learning (EML) at Helmholtz Munich are inviting applications for a fully funded PhD position focused on developing the next generation of integrated AI systems for scientific discovery. The doctoral project offers an opportunity for aspiring researchers to contribute to cutting-edge work at the intersection of artificial intelligence, explainable machine learning, multimodal foundation models, and AI for Science.
Applications receiving full consideration must be submitted by August 10, 2026, at 23:59 CET, although applications may continue to be reviewed until the position is filled.
Advancing Trustworthy AI for Scientific Research
While modern artificial intelligence has produced increasingly powerful models, scientific research requires AI systems capable of integrating diverse sources of information, external tools, domain expertise, and human feedback in transparent and reliable ways.
This PhD project will investigate how scientific AI systems can become more:
- Reliable.
- Interpretable.
- Adaptable.
- Robust.
- Uncertainty-aware.
The research will focus on understanding how uncertainty, assumptions, errors, limitations, and data provenance propagate across integrated AI systems and how these systems can be effectively evaluated, updated, and inspected throughout scientific workflows.
Research Areas
The selected PhD candidate will contribute to several advanced research topics, including:
- Explainable AI (XAI) and mechanistic interpretability for unimodal and multimodal foundation models.
- Multimodal alignment and representation learning, including vision-language models (VLMs), multimodal reasoning, and foundation models.
- Reliable adaptation and continual learning, including parameter-efficient fine-tuning and model adaptation techniques.
- AI for Science, including agentic scientific workflows, benchmarking, uncertainty estimation, and applications involving biological and medical datasets.
The exact research direction will be developed collaboratively between the successful candidate and the supervising research team.
Collaborative Research Environment
The doctoral researcher will become part of an internationally connected research community spanning:
- The Institute for Explainable Machine Learning (EML) at Helmholtz Munich.
- The Chair of Interpretable and Reliable Machine Learning at the Technical University of Munich.
- An international network of collaborating researchers and institutions.
The programme offers opportunities to engage in interdisciplinary research combining machine learning, computational science, and scientific discovery.
Eligibility Requirements
Applicants should possess strong academic preparation and technical expertise in artificial intelligence or related disciplines.
Eligible candidates should have:
- A Master’s degree or equivalent qualification in Computer Science, Machine Learning, Mathematics, Statistics, Physics, Engineering, or a closely related field.
- A strong foundation in machine learning.
- Strong programming skills and experience using modern machine learning frameworks.
- An interest in explainable AI, reliable machine learning, multimodal learning, foundation models, AI agents, or AI for Science.
- Excellent communication skills.
- The ability to conduct independent research while contributing effectively within collaborative teams.
Previous publications in leading machine learning, computer vision, or natural language processing conferences are considered an advantage but are not mandatory.
The research team particularly welcomes applicants who enjoy combining conceptual research with technical implementation and who are interested in helping shape emerging research directions in trustworthy scientific AI.
Application Requirements
Applicants must submit their application as one consolidated PDF document containing:
- A current curriculum vitae (CV).
- Academic transcripts and degree certificates.
- A short research statement outlining research interests, relevant experience, and alignment with the project.
- Contact information for two academic or professional references.
Please send your application with the subject line containing [PhD 2026 EML] to:
[email protected]
Priority Application Deadline
Candidates are encouraged to submit their applications before the priority deadline.
Important dates:
- Priority deadline: August 10, 2026
- Time: 23:59 Central European Time (CET)
Applications submitted by this deadline will receive full consideration. Additional applications may be reviewed afterwards until the position is filled.
Opportunity to Shape the Future of AI for Science
This fully funded PhD position offers an outstanding opportunity for aspiring researchers to contribute to the development of trustworthy AI systems capable of supporting future scientific discovery. Working within two of Germany’s leading research institutions, the successful candidate will engage in pioneering research addressing some of the most important challenges in explainable artificial intelligence, multimodal learning, and AI-enabled scientific innovation.
