Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Humboldt-Stiftung Foundation
- Forschungszentrum Jülich
- Fraunhofer-Gesellschaft
- DAAD
- Nature Careers
- Technical University of Munich
- Heidelberg University
- Charité - Universitätsmedizin Berlin •
- Leibniz
- Max Planck Institute for Nuclear Physics, Heidelberg
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg
- University of Bonn •
- 2 more »
- « less
-
Field
-
results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
-
and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be considered an asset Proven record in publication
-
times through higher parallelization and enable targeted stimulation of hardware faults by adjusting the models. To this end, a simulation environment based on a virtual prototype will be developed using
-
Identify new applications for Machine Learning in science, engineering, and technology Develop, implement and refine ML techniques Implement parallel ML training on the High Performance Computers Engage in
-
, molecular biotechnology and computational sciences. The modern Campus harbors the buildings of the science faculties and institutes, in direct vicinity to the University Hospital Heidelberg, the Max Planck
-
Max Planck Institute for Nuclear Physics, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 11 days ago
Hinton ) at the Max Planck Institute for Nuclear Physics in Heidelberg (Germany) offers Postdoc positions (m/f/d). The Division is engaged in a broad programme of experimental and observational activities
-
- into a GPU-enabled and parallel code to run efficiently on state-of-the-art exascale hardware Designing implementations and reviewing community contributions of library features and new statistical
-
this, these simulations need to be massively parallelized. The objective of this thesis is to implement and evaluate different contingency parallelization approaches using our group's computational infrastructure
-
on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
-
. You will employ the trypanosome model established in our group to study its swimming behavior in soft tissue-like surroundings. This project is a part of the DFG-SPP 2332 priority program “Physics