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behaviour and provides active personalised learning for improved instant decision making. Key beneficiaries are expected to be construction industry stakeholders, for example, project owners, architects, engi
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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tasks will be to: Genetic engineering of bacteria. Phenotypic characterisation of engineered strains. Teach and supervise BSc and MSc student projects. You must have a two-year master's degree (120 ECTS
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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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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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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. 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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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