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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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Policy Implications and Recommendations Case Studies of Successful Innovation Funding Methods The project will employ a combination of methods, including machine learning (ML) and generative AI (GenAI
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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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learning cultures can be developed in vocational education. While the PhD fellow is expected to contribute to this overarching agenda, the position also allows and requires the development of an independent
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terms of research The proposed PhD project will be situated within the broader framework of PRAXIS, which investigates how creative, experimental and practice-based learning cultures can be developed in
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: SIMD performance engineering. Machine Learning. Communication-efficient
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partners with whom we generate and study clinical effects of colostrum-replacer for compromised piglets who are unable to acquire own mothers’ colostrum. The majority of the experimental in vivo work is
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. Application procedure Before applying the candidates are advised to read the Faculty information for prospective PhD students and the SDU information on how to apply . Assessment of the candidates is based
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synthetic fuel reactors. Tasks include gas handling, system diagnostics, thermal integration, and performance evaluation under variable power inputs. Data Analysis and Machine Learning: Collect and process
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. You should have a strong academic background in engineering, applied mathematics, or computer science, combined with a clear interest in scientific programming, machine learning, and data analytics