Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Technical University of Munich
- Nature Careers
- Forschungszentrum Jülich
- Leibniz
- Fritz Haber Institute of the Max Planck Society, Berlin
- Heidelberg University
- Helmholtz-Zentrum Berlin für Materialien und Energie
- Max Planck Institute for Brain Research, Frankfurt am Main
- Max Planck Institute for Infection Biology, Berlin
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- Max Planck Institute for Radio Astronomy, Bonn
- Max Planck Institute for the Structure and Dynamics of Matter, Hamburg
- University of Tuebingen
- 3 more »
- « less
-
Field
-
machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
-
data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
-
algorithmic algebra. For more information about the TUM Department of Mathematics, please visit our website: https://www.math.cit.tum.de/en/math/home/. The position is a full-time position (100%), initially
-
, collaborating with several research groups working in related fields, particularly in algebraic geometry and algorithmic algebra. For more information about the TUM Department of Mathematics, please visit our
-
Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | 3 months ago
Experience in HPC computation (application and algorithm/code development) Willingness to closely collaborate with experimentalists and theoretician. Joint research approach of all ERC synergy team members
-
. 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
-
algorithms into an existing framework, with a focus on efficiency, as well as creation and execution of relevant simulation pipelines: from real data to mathematical and clinically actionable results
-
that algorithmic parameters are tuned so that the over-approximation of the computed reachable set is small enough to verify a given specification. We will demonstrate our approach not only on ARCH benchmarks, but
-
methods, machine learning algorithms, and prototypical systems controlling complex energy systems like buildings, electricity distribution grids and thermal systems for a sustainable future. These systems
-
MesaPD to solve complex multiphysics problems. The coupling is done across package boundaries. This also requires more sophisticated approaches in load-balancing. Finally, the newly developed algorithms