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machine learning for safe and optimal control of cyber-physical systems. The projects are expected to be funded by the VILLUM INVESTIGATOR project S4OS (“Scalable analysis and synthesis of safe, secure and
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the research project (DIGIFABA) group combining expertise in agronomy, statistics, and machine learning consisting of three PhD fellows, two postdocs, the supervisor and co-supervisor from the department and a
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, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction
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virtual/augmented/extended (VR/AR/XR) environments to support learning of scientific concepts and practices at the university-level. This work package investigates how such cutting-edge educational
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background in CMOS/VLSI design, computer architectures (preferred RISC-V architecture), and deep learning principles. Experience with industry-standard EDA tools such as Cadence suite: Genus, Virtuoso, Spectre
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Applicants should hold a relevant MSc degree in electronics, electrical engineering, computer engineering, or related fields. Required Qualification: Solid background in digital CMOS design and deep learning