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Field
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motion and the viewing perspective of the observer (Nikolaidis et al, 2016). This project will develop continuous models of action legibility using these sources of information from data collected in a
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experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter
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science, computer science, applied mathematics or a related field a strong background in machine learning, material modeling, and metals processing, modeling and simulation (This will be a clear advantage
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., extracted from the collected data) and “predications” (generated by the C2 operational model). The tool will detect subtle discrepancies indicative of stealthy data manipulation with zero false alarms
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modeling & environmental risk assessment. Numerical simulation techniques for hydrogeological systems. Advanced uncertainty quantification for robust modeling. Scientific communication, including
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/Simulink/Python for modelling, simulation, and control design. Experience with genset systems, hybrid powertrains, or real-time control applications is highly desirable. A practical interest in system
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gyre (SPG) and Arctic and Antarctic sea ice cover, involving processes on smaller scales, which are often not well represented in modelling efforts focusing on the large scale tipping elements
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with researchers from climate physics, hydrology, sustainability science and complex systems dynamics and apply a range of different models. Starting from the recent AMOC tipping simulations performed
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components are in use. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process
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tasks will be to: Develop and implement machine learning models for dynamic simulations of renewable power systems Develop comprehensive guidelines for verifying and testing dynamic equivalents Integrate