The Faculty of Physics at the University of Vienna invites applications for a University assistant predoctoral/PhD Candidate position in the Computational Materials Discovery group of Professor Reinhard Maurer, focusing on machine learning methods for electronic structure theory.
Position Details
- Location: University of Vienna, Austria
- Duration: 2 years (initially limited to 1.5 years, automatically extended to 2 years if not terminated within first 12 months)
- Start Date: February 1, 2026
- Salary: EUR 3,714.80 gross per month (full-time basis, 14x p.a.) with increases for professional experience
- Contact: Prof. Reinhard Maurer
Research Opportunity
This is an opportunity to work towards a PhD in physics and to conduct world-leading research and teaching in molecular simulation and computational materials discovery.
Research Context
Fuel cells, photovoltaic devices, photocatalytic converters – they all are crucial elements in delivering decarbonization and sustainable energy production at a global scale. They fundamentally involve energy transfer and chemical dynamics at interfaces where molecules, electrons, and light interact. The underlying mechanisms of ultrafast dynamics at surfaces triggered by light or electrons are not well understood, limiting our ability to design optimal photocatalyst materials.
This project is part of a large initiative that aims to tackle this challenge by developing and applying new software tools that combine:
- Machine learning methodology
- Electronic structure theory
- Molecular dynamics methodology
to simulate ultrafast chemical dynamics at surfaces and in materials.
Your Research Focus
As a university assistant (praedoc), you will focus on the development of novel machine learning representations of electronic structure and quantum operators. These surrogate models will be combined with mixed quantum-classical dynamics simulation approaches to perform chemical dynamics simulations at unprecedented scale.
Key Objectives
- Contribute to the development of broadly applicable electronic structure methods
- Develop new software and machine learning methods
- Enable high throughput screening of optimal photocatalyst materials
- Reveal important mechanistic insights into ultrafast dynamics and measurable spectroscopic properties
- Work in close collaboration with a broad network of international collaborators
Your Responsibilities
Research:
- Actively participate in curiosity-driven research project in machine learning in computational materials science
- Present your research plan to the faculty and complete a dissertation agreement within 12-18 months (reviewed and adapted annually)
- Work on your dissertation with a high degree of independence paired with social awareness
- Continuously stay informed about the state of the art in your field
- Contribute to outreach through publications, conference presentations, and public activities
Teaching & Administration:
- Contribute to teaching (exercise classes) within the provisions of the collective bargaining agreement
- Fill administrative tasks, contributing to the success and self-organization of the group
Required Qualifications
- Master’s degree or Diploma in Physics (Note: Bachelor’s degree with Honours may be considered in exceptional cases)
- Experience in academic writing
- Interest and background in:
- Condensed matter theory
- Electronic structure theory
- Excellent command of written and spoken English
- Experience with:
- Programming (e.g., Python, Julia)
- Simulation methods (e.g., molecular dynamics)
- Modern machine learning methods
What We Offer
Research Environment:
- Join a large, international and interdisciplinary research group that provides a collaborative and supportive environment
- Member of the Vienna Doctoral School in Physics
- Member of the faculty research group Computational Materials Physics
- Opportunities to connect with all relevant top research groups in the world
- Present research at international and national conferences
Skills Development:
- Acquire important transferable skills such as software development and project management
- Access to over 600 free training courses through Vienna Doctoral School and human resources department
Working Conditions:
- Inspiring working atmosphere in an international academic team
- Healthy and fair working environment
- Good public transport connections
- Fair salary with increases for professional experience
- Equal opportunities employer - diversity is an asset
About the Group
In the Maurer group, we aim to develop computational simulation methodology to study quantum phenomena at surfaces with applications ranging from photocatalysis, to nanotechnology and electrochemistry. Our goal is to combine electronic structure theory, molecular and quantum dynamics methodology, and machine learning methods to achieve an accurate yet computationally feasible description of complex phenomena in materials and at solid/gas and solid/liquid interfaces.
Distinguishing Features of This Position
This position has a stronger emphasis on machine learning methods compared to our other PhD positions. You will be at the forefront of developing novel ML representations for quantum mechanical properties, making this ideal for candidates with:
- Background or strong interest in machine learning
- Desire to develop new methodologies at the intersection of ML and quantum physics
- Interest in high-throughput computational screening approaches
Application Process
Application Materials:
- Scientific curriculum vitae
- Summary of your previous academic and research achievements (tell us about your moments of professional pride)
- Short statement on your research interests for the future / motivation letter (tell us what you dream about, scientifically)
- Bachelor’s and Master’s degree certificates (an excellent academic degree is a good entrance statement for this position)
Contact for Questions: Prof. Reinhard Maurer Email: reinhard.maurer@univie.ac.at
Note: We strive to create a fair and equitable work environment, where diversity is an asset and individuals can flourish.