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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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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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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | about 3 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 | 5 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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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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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
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- 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