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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
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sintering press with selected copper pastes, followed by detailed characterization of the resulting interfaces in terms of porosity, thermal and mechanical integrity. In parallel, simulation models will be
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organizing multiple parallel projects Practical knowledge and hands-on experience in molecular biology laboratory work Leadership skills and proven experience in staff management, combined with strong team
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(e.g., RNA-Seq, 5mC / 5hmC-Seq, DNA-Seq, ATAC-Seq, CUT&RUN / CUT&Tag, 16S) Experience in automated sample preparation, as well as project management skills to organize multiple parallel projects
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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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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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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | about 20 hours ago
domains of population research by combining the methods and perspectives of computational sciences, social and behavioral sciences, and statistics. The field has emerged in parallel with rapid technological
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Max Planck Institute for Demographic Research, Rostock | Rostock, Mecklenburg Vorpommern | Germany | 13 days ago
, social and behavioral sciences, and statistics. The field has emerged in parallel with rapid technological improvements in computing, the spread of Internet and mobile technologies, and the increased
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using the programming language Fortran. Experience or willingness to run numerical models on parallelized supercomputers. Experience in the analysis of model output using Python or a similar high-level
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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