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Job Description Are you passionate about renewable energy and eager to apply machine learning to real-world challenges? Join our research team at DTU and work on groundbreaking advancements in
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understanding of the process, statistics and data analytics shall be applied to link the different conditions to the likelihood of microcracks occurring. The severity of microcracks may also be studied in
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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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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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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
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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