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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
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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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and machine learning, you will help develop new methods for understanding complex failure mechanisms—an area where existing industrial knowledge remains limited. The project will be executed in three
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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mechanics (nonlinear beam theory, fluid-structure interaction) Desire to develop interdisciplinary expertise across hydrodynamics and structural mechanics. Experience with or willingness to learn: Programming
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) or a similar degree with an academic level equivalent to a two-year master's degree. Additionally, you should meet the following qualifications You have a strong analytical skills and background in
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could include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success Policy Implications and Recommendations
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can learn more about the recruitment process here . Applications received after the deadline will not be considered. All interested candidates irrespective of age, gender, disability, race, religion or
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of teaching experience Academic Diplomas (MSc/PhD) You can learn more about the recruitment process here . Applications received after the deadline will not be considered. All interested candidates irrespective
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in the Danish energy sector. As a PhD candidate, you will join the Energy Markets and Analytics (EMA) Section within the Division for Power and Energy Systems (PES). The EMA Section is renowned for its