Quantum Computing Jobs

Department: Department of Mathematics and Computer Science
contract duration: limited to 3 years
Salary / pay grade: E 13 TV-L FU
Working hours: 75 %
Requisition ID: 883
Application deadline: 21.09.2026
.buttontext45cddda1c9cea23f a{ border: 1px solid transparent; } .buttontext45cddda1c9cea23f a: focus{ border: 1px dashed #0066cc !important; outline: none !important; }
Salary calculator TV-L
What You Can Expect in Your New Role

Contribution to research on the integration of machine learning into quantum chemical methods and molecular simulation. A particular focus will be on the development, application, and evaluation of modern machine-learning methods for quantum chemical problems and molecular simulations, with a specific emphasis on research into variational quantum Monte Carlo methods for molecular simulation using neural-network-based ansatzes.
The position will play a central role in the maintenance, further development, and documentation of the DeepQMC software and the associated development and simulation infrastructure. This includes the implementation and evaluation of new methods, the further development of existing functionalities, and ensuring sustainable and reproducible software development.
In addition, the position will involve the academic supervision of doctoral researchers and MSc students, as well as the development of independent scientific research questions and new project ideas. The employee will contribute to scientific publications, conference presentations, and third-party funding proposals, and will participate in national and international collaborations as well as other scientific activities of the research group.

The position includes teaching duties in accordance with the applicable regulations governing teaching obligations. The position is intended to support the individual's own academic qualification (habilitation).

Key Requirements

Completed university degree (Master's or equivalent) and PhD in mathematics, computer science, physics, or a related discipline.

Desirable
At least four years of full-time professional experience in academic research, as well as evidence of relevant expertise through publications in leading peer-reviewed scientific journals and experience in developing machine learning software, are required.
Internationally leading research record in quantum Monte Carlo
Familiarity with methods of computational quantum chemistry
Experience in the development and application of machine-learning pipelines
Strong knowledge of software frameworks for implementing GPU-accelerated computations, in particular JAX and PyTorch
Knowledge of HPC environments and experience in efficiently scaling scientific software on computing clusters
Experience in managing and further developing open-source software projects, as well as in creating and maintaining scientific and technical software documentation
Teaching experience at universities or comparable academic institutions
Presentation of own research results at international scientific conferences
Gender and Diversity Competence
Benefits and Other Advantages
Salary in line with the collective agreement for the civil service at the state level (TV-L FU), plus additional one-off annual payment
Flexible working hours and mobile working by arrangement, where possible
Thirty days of vacation (based on a five-day working week)
Office closed on December 24 and December 31
If you are interested in what we have to offer,

then you can send your application materials to us directly. Simply submit your application exclusively via our career portal by clicking the “Apply now” button. Unfortunately, we cannot consider applications by post or by e-mail.

You can also get in touch with Ms. Sander (manuela.sander@fu-berlin.de).

h1 { background-color: #f1f1f1; border-left: 15px solid #9c0; padding: 15px; }
@media print { body { -webkit-print-color-adjust: exact; } .footerColumnsShell{ display: none!important; } .header{ display: none!important; } .btn { display: none!important; } #search-wrapper{ display: none!important; } #similar-jobs{ display: none!important; } .backgroundimage24f79c41-e2f0-417d-8251-f { display: block!important, } .printicon{ display: none!important; } .large-image-component { display: none; } #back-to-search { display: none; } .joblayouttoken { margin-bottom: 25px; } }