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system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models and machine
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digital twin capabilities to all respective levels of the manufacturing system, combining machine learning, decision-making, and optimisation with Digital Twins. This should allow systems to dynamically
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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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The project involve conducting high-quality research at the intersection of thermo-fluids science, AI/machine learning and optimization. We envision that: You have an open mind and can think creatively in
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Machine Learning modeling and optimization Generative AI modeling (closed-loop AI tools, or related AI-tools) Design of molecular binders Interest in mentoring MSc students and helping coordinate with
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Job Description To support advancements in artificial intelligence (AI), machine learning, and data science at the University of Southern Denmark, the Maersk Mc-Kinney Moller Institute invites
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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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proficient in ROS/ROS2, Python and/or C++/C# Knowledge and/or experience within one or more of the fields of acoustic sensing, hydrodynamics, and machine learning is a plus. You have strong communication