100 parallel-and-distributed-computing positions at University of Southern Denmark in Denmark
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postgraduate (PhD) levels. Responsibility for program and curriculum development. The capacity to maintain and develop relationships with relevant stakeholders and to actively promote and participate in DIAS
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Neural Networks (SSM-SNNs). The project includes the co-design and integration of a RISC-V processor for hybrid neuromorphic computing. The research aims to develop ultra-low-power computing chips
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University of Southern Denmark, IMADA - Department of Mathematics and Computer Science Position ID: SDU -ASSISTPROF1 [#26735, 3023] Position Title: Position Type: Non tenure-track faculty Position
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three years. Employment stops automatically at the end of the period.(the 5+3 programme ). Further information about the PhD programme at the Faculty of Science can be found at the homepage
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existing activities in CMOS circuit design, neuromorphic computing, cryogenic electronics, and spintronic-based computing systems. The role also contributes to Denmark’s and Europe’s strategic ambitions
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project. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power
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Master’s programme. If you have practical experience in subject areas relevant to the proposed research project, it is naturally an advantage, but it is neither required nor expected. A PhD is a research
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basic research. We are happy to move beyond traditional disciplines and work to develop new technologies and sciences across physics, chemistry, pharmacy, mathematics, computer science and biology. The
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algorithmic aspects of cheminformatics. The position is founded by the Challenge Programme of the Novo Nordisk Foundation: “Mathematical Modelling for Microbial Community Induced Metabolic Diseases ”, led by
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on conventional computing platforms such as GPUs, CPUs and TPUs. As language models become essential tools in society, there is a critical need to optimize their inference for edge and embedded systems