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21 Aug 2025 Job Information Organisation/Company TU Dresden Research Field Computer science » Other Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1) Established Researcher
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computing, domain-specific multi- and manycore architectures, networks-on-chip (NoCs), methods and algorithms for application parallelization, simulators and virtual platforms for application- and
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Germany Application Deadline 21 Sep 2025 - 21:59 (UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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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
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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
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, 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
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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
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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
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Max Planck Institute for Nuclear Physics, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 26 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
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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