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Danfoss, you will combine thermofluid modelling, reduced-order multiphysics methods, and nonlinear rotor dynamics analysis to develop predictive modelling tools that support the industrial design of
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, Greenland glaciology, statistical analysis of proxy data. You should have good analytical and problem-solving skills and be motivated to develop quantitative approaches that connect geological records of past
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on ocean and coastal dynamics, marine processes, and their response to environmental change through a combination of field observations, data analysis, and numerical modelling. The position is embedded
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sensor data, and (iii) sophisticated fatigue analysis models combining advanced fracture mechanics with crack initiation and growth simulation. Additionally, you will explore fatigue mitigation strategies
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Very good knowledge of English (written and oral) Good communication skills (scientific community, the public, stakeholders, and managers) Experience in data analysis and statistical tools Preferable
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numerical modelling, simulation, optimization, control, or engineering-data analysis. Good programming skills in Python, MATLAB/Simulink, or a comparable scientific computing environment. A fundamental
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. The project will provide training in paleoceanography and paleoclimate research, including the development and interpretation of proxy records, quantitative analysis of environmental data and integration
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-driven modelling. Experience with numerical modelling, simulation, optimization, control, or engineering-data analysis. Good programming skills in Python, MATLAB/Simulink, or a comparable scientific
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science, communication, political science, or journalism/media studies. The PhD student will be involved in all aspects of the research, from conception, execution, analysis, publishing to public dissemination. The ideal
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, data analysis, and dissemination of research findings. The project combines perspectives from systemic innovation, integrated care, and health economics. The PhD student will collaborate closely with