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be applying methods such as sensitivity analysis, robust optimization, and stochastic modelling as you work on your project. You will be seconded with the Chalmers University of Technology (Sweden) and
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focus on the topics of infrastructure adaptability and scalability, PtX technologies, de-risking strategies, and flexibility assessment. You will be applying methods such as sensitivity analysis, robust
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, health and well-being (PtD/P, HECA, pro-active real-time safety technology), and lean construction methods (e.g., TPTC, LPS, LBS) Familiarity with building, infrastructure, and/or off-shore construction
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. Cox. The position is available starting 1 December 2025. The successful candidates will work on developing new theoretical models and computational methods to investigate the emergence and properties
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for the position. About DTU Engineering Technology We work at the forefront of university-industry collaboration and develop tools and methods for technology implementation. We are organized into 8 sections all
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, might be for you! Responsibilities and qualifications Working with colleagues in the MULTIBIOMINE project, you will develop computational methods that use novel strategies to uncover hidden features in
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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 systematic phases
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derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development for scalable and green AI. Use cases in
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deformation. Responsibilities Develop scientific machine learning methods in close collaboration with team members specializing in experimental techniques and materials science. Utilize unique experimental data
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testing dynamic equivalencing methods for power system dynamic simulations and integrating these into commercial simulation tools. Dynamic equivalents are simplified representations of complex power system