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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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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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(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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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | 18 days 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 | about 2 months 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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the REACT Doctoral Network. In this context, we seek a highly motivated researcher with a proven track record in parallel programming models for CPS, high-level compilers, system and computer architecture and
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of Parallel computing Independent, goal-oriented and responsible working style What you can expect Insight into exciting research projects Cutting-edge research thanks to top technical equipment Practical
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Start your online nomination or continue your nomination in "My Humboldt" Start online nomination Continue nomination Germany's most valuable research award The Humboldt Professorship is financed by the Federal Ministry of Education and Research. It enables the holder to conduct...
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Exploiting quantum systems with more than two energy levels (qudits) Scaling compilation approaches to utilize the processing power of parallel systems/supercomputers To learn more about our previous work