PhD Scholarship Position: The Technical University of Munich (TUM) invites applications for a PhD position starting January 2026 at the TUM School of Computation, Information and Technology. The successful candidate will work under the supervision of Prof. Alessio Gagliardi in the Department of Electrical Engineering.
The research focuses on developing machine learning-driven models and dynamical systems approaches to simulate and predict the long-timescale structural evolution and catalytic behavior of metal–organic frameworks (MOFs) in electrochemical environments.
PhD Scholarship Position – Technical University of Munich (TUM)Accelerated Modeling of Metal–Organic–Framework Dynamics and Electrolyte Interactions through Machine Learning and Koopman Theory
Summary Table
| Field | Details |
|---|---|
| Designation | PhD Position |
| Project Title | Accelerated Modeling of Metal–Organic–Framework Dynamics and Electrolyte Interactions through Machine Learning and Koopman Theory |
| Research Area | Machine Learning, Atomistic Modelling, Koopman Theory, Electrochemical Catalysis, MOFs |
| Institution | Technical University of Munich (TUM) |
| Department | Department of Electrical Engineering, Simulation of Nanosystems for Energy Conversion |
| Supervisor | Prof. Alessio Gagliardi |
| Location | Munich, Germany |
| Start Date | January 2026 |
| Duration | 3 Years |
| Salary | 75% of TV-L E13 position |
| Application Email | alessio.gagliardi@tum.de |
| Last Date to Apply | Open until filled |
Designation
PhD Researcher (75% TV-L E13, 3 years)
Research Area
- Machine Learning for Physical Systems
- Atomistic and Molecular Dynamics Modeling
- Koopman Operator Theory and HAVOK Analysis
- Electrochemical Catalysis and MOF Dynamics
Location
Technical University of Munich (TUM)
TUM School of Computation, Information and Technology
Department of Electrical Engineering
Munich, Germany
Eligibility / Qualification
Required Qualifications:
- Experience with machine learning methods
- Knowledge and practical experience in atomistic modeling
- Hands-on skills in data generation and engineering
- Excellent oral and written communication skills in English
- Ability and willingness to work collaboratively in a research team
Job Description
The selected PhD candidate will:
- Develop surrogate models for molecular dynamics of MOFs using Temporal Fusion Transformers (TFTs) to accelerate sampling of rare events (e.g., ligand dissociation, phase transitions).
- Implement adaptive model-switching strategies using Koopman operator theory and HAVOK analysis to ensure long-term accuracy in simulations.
- Apply developed methods to study how electrolyte additives influence MOF reconstruction and catalytic activity under varying operational conditions such as pH and temperature.
- Collaborate with interdisciplinary teams and contribute to high-impact publications in the areas of computational chemistry, machine learning, and catalysis.
How to Apply
Interested candidates should submit the following documents as a single PDF to Prof. Alessio Gagliardi via email at alessio.gagliardi@tum.de:
- Curriculum Vitae (CV) with a detailed list of research publications
- Cover letter outlining research interests and relevant experience
- Names and contact information of three references
Last Date to Apply
Applications will be reviewed on a rolling basis until the position is filled.
Start date: January 2026
