DLR's Institute of Frontier Materials in Space investigates fundamental physical processes in materials, from the microscopic scale to macroscopic properties, both in model systems for new materials and in applied materials, on Earth and in space. To this end, we combine the development of space flight hardware, experiments on research rockets, parabolic flights and in the drop tower, as well as modelling with theory and simulation on all scales in order to understand the influence of gravity on physical processes.
What to expect
Quantum hardware promises advantages in various machine learning methods for specific problems.
Progress in this area depends on understanding the optimisation or energy landscapes that can be used to describe the problem under consideration. Here, the physical problem of a non-classical energy landscape and its impact on the effectiveness of machine learning algorithms will be considered.
Your tasks
- use of conventional and quantum computer-based calculations for machine learning
- investigation of the energy landscapes of different machine learning methods for classical and non-classical methods
- analysis of the results and publishing them in conference papers and scientific articles
Your profile
- completed scientific university degree (Master / Diploma Uni) in computer science, mathematics, natural sciences (e.g. physics or mathematics) or engineering or other degree programmes relevant to the position
- initial experience in at least one of the following research areas: machine learning, quantum information theory, development of quantum algorithms, use of quantum computing to solve materials science optimisation problems
- ability to work in an international team of scientists, technicians, students and international co-operation partners
- very good written and spoken English skills
We look forward to getting to know you!
If you have any questions about this position (Vacancy-ID 2377) please contact:
Matthias Sperl
Tel.: +49 2203 601 3434
