About the team: This is an opportunity to work towards a Ph D in physics and to conduct world-leading research and teaching in molecular simulation and computational materials discovery.
Fuel cells, photovoltaic devices, photocatalytic converters - they all are crucial elements in delivering decarbonization and sustainable energy production at a global scale within the coming decades.
They all fundamentally involve energy transfer and chemical dynamics at interfaces where molecules, electrons, and light interact to deliver a certain function.
The underlying mechanisms of ultrafast dynamics at surfaces triggered by light or electrons are not well understood, which, for example, limits our ability to design photocatalyst materials that deliver optimal light absorption, catalytic activity, and energy transport.
Molecular simulation methods and quantum theoretical calculations in principle can address this but have hitherto struggled with tackling such challenging systems.
With the emergence of machine learning methods in the physical sciences, things are rapidly changing.
This project is part of a large initiative that aims to tackle this ambitious challenge by developing and applying new software tools that combine machine learning methodology, electronic structure theory, and molecular dynamics methodology to simulate ultrafast chemical dynamics at surfaces and in materials.
Your personal sphere of influence: As a university assistant (praedoc), you will be part of the Computational Materials Discovery group of Professor Reinhard Maurer.
This project 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 an unprecedented scale.
You will contribute to the development of broadly applicable electronic structure methods, new software, and machine learning methods with the specific goal to enable high throughput screening of optimal photocatalyst materials and to reveal important mechanistic insights into ultrafast dynamics and measurable spectroscopic properties.
This will be done in close collaboration with a broad network of international collaborators.
The employment duration is 3 years.
Initially limited to 1.5 years, the employment relationship is automatically extended to 3 years if the employer does not terminate it within the first 12 months by submitting a non-extension declaration.
With appropriate work progress, an extension to a total maximum of 4 years is possible.
Your future tasks: You will actively participate in research, teaching & administration.
This means: You are involved in a curiosity-driven research project in the field of machine learning in computational materials science.
You will present your research plan to the faculty and complete a dissertation agreement within 12-18 months.
This will be reviewed and adapted on an annual basis.
You will work on your dissertation and towards its completion in time.
We expect a large degree of independence paired with a high level of social awareness as the goal will only be achieved in a team.
You will contribute to teaching (exercise classes) within the provisions of the collective bargaining agreement.
You will fill some administrative tasks, contributing to the success and self-organization of the group for research, teaching and administration.
You will continuously stay informed about the state of the art in your field.
You will contribute to outreach by publications, conference presentations and public activities.
Candidate profile: You have completed your Master's degree or Diploma in physics.
You have experience in academic writing.
You have an interest and background in condensed matter theory and electronic structure theory.
You have an excellent command of written and spoken English.
You should have experience with programming (e.g.
Python, Julia), simulation methods (e.g.
molecular dynamics) and modern machine learning methods.
What we offer: In the Maurer group, we aim to develop computational simulation methodology to study quantum phenomena at surfaces with applications ranging from photocatalysis to nano
technology 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.
You will join a large, international and interdisciplinary research group that provides a collaborative and supportive environment.
Our team is member of the Vienna Doctoral School in Physics and the faculty research group Computational Materials Physics.
Ph D students in the group acquire important transferable skills such as software development and project management.
You will present your research at international and national conferences.
Inspiring working atmosphere: You are a part of an international academic team in a healthy and fair working environment.
Potential for development: Success in life depends on what you make of it, but if you are ambitious and self-driven, there are plenty of opportunities to connect you to all relevant top research groups in the world.
Good public transport connection: Your new workplace is easily accessible by public transport.
Internal further training & Coaching: The Vienna Doctoral School as well as the Department of Human Resources offer plenty of opportunities to grow your skills in over 600 courses to choose from - free of charge.
Fair salary: The basic salary of EUR 3.776,10 (on a full-time basis, 14x p.a.) increases if we can credit professional experience.
Equal opportunities for everyone: We strive to create a fair and equitable work environment, where diversity is an asset and individuals can flourish.
It is that easy to apply: Your scientific curriculum vitae.
A summary of your previous academic and research achievements.
Tell us about your moments of professional pride.
A short statement on your research interests for the future (motivation letter).
Tell us what you dream about, scientifically.
Your Bachelor's and Master's degree: an excellent academic degree is a good entrance statement for this position.
Note, that while a Master's degree is the usual legal prerequisite for a Ph D position at the University of Vienna, we can consider Bachelors (preferentially with Honors) in exceptional cases, too.
If you have any content questions, please contact: Reinhard look forward to new personalities in our team! The University of Vienna has an anti-discriminatory employment policy and attaches great importance to equal opportunities, the advancement of women and diversity.
We lay special emphasis on increasing the number of women in senior and in academic positions among the academic and general university staff and therefore expressly encourage qualified women to apply.
Given equal qualifications, preference will be given to female candidates.
University of Vienna.
Space for personalities.
Since 1365.
Data protection Application deadline: 01/30/2026 Prae Doc Apply now
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