Sort by
Refine Your Search
-
Employer
- Technical University of Munich
- DAAD
- Max Planck Institute for Demographic Research (MPIDR)
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg
- Max Planck Institute for Mathematics in the Sciences
- Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr
- Nature Careers
- University of Hamburg
-
Field
-
[Wissenschaftszeitvertragsgesetz, WissZeitVG]).Application deadline: 16.12.2025Scope of work: full-time position suitable for part-timeThe goal of this project is to develop and analyze application-oriented benchmarks for Rydberg
-
classical topics in numerical analysis, such as the analysis of nonlinear PDEs or the development of new solver- or coupling-methods including their convergence analysis, but also modeling and simulation
-
. The project’s overarching goal is the development of digital quantum algorithms for the simulation of non-abelian lattice gauge theories. We are looking for highly motivated individuals, with the desire
-
- PhD student in quantitative verification interested in co-developing Automata Tutor - main developer of Automata Tutor Positions in the Formal Methods for Software Reliability group of TU Munich led by
-
smart grid). While there has been tremendous progress in formal verification of cyber-physical systems, existing approaches still require expert knowledge. The main goal of this project is to develop
-
transformation, tumour evolution, metastasis or drug response and resistance. See examples in: Nature 2018 Feb 1;554(7690):62, Nature Reviews Cancer 2020, Oct;20(10):573. Cancer Cell. 2023 Jul 10;41(7):1327; Nat
-
computer science with very good results - Interest on topics around the area of distributed systems and data management - Basic knowledge in distributed systems and graph algorithms is desired - Hand-on experience
-
) Paternal age effect on offspring outcomes.2) Keen interest in developing theory of reproductive ageing, in conjunction with existing theories on fertility and health.3) Expertise in population data (e.g
-
such as pre-ignition risks, material compatibility, and storage under high pressure. To address these challenges, we will develop novel techniques for provably safe reinforcement learning. This project is
-
exploration. These methods will be developed together with the company CargoKite (https://cargokite.com/ ), which develops a ship for autonomous, highly flexible global container transportation. The transport