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and modeling the environmental fate of chemicals and microplastics from footwear materials under relevant life-cycle and end-of-life scenarios. Conducting laboratory experiments to analyze microplastic
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partner You will combine laboratory-based analytical work with modelling approaches to identify trends in material composition, chemical presence, and contamination across the product lifecycle
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research stay at the University of Maastricht (UM), which is the double degree partner You will combine laboratory-based analytical work with modelling approaches to identify trends in material composition
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, ultimately enabling the development of machine-learning models for the inverse design of improved electrolyte materials. You will have a demonstrated aptitude for computational chemistry or related research
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modeling of complex information systems, and SDU and the Royal Danish Defence College’s established intelligence studies and practice. By introducing computational modeling to traditional intelligence
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, atomic scale and multi-physics modelling, autonomous materials discovery, materials processing, and structural analyses. We also focus on educating engineering students at all levels, ranging from BSc, MSc
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latest predictive and generative AI for materials, we can offer you the best possible foundation. We seek two highly motivated and talented PhD students to join our group at DTU Compute, and we offer
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performance utilizing reliable and computationally efficient numerical structural models. To support the condition (state) assessment, you will also explore the use of advanced estimators (e.g., Kalman Filter
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of the nonlinear structural performance utilizing reliable and computationally efficient numerical structural models. To support the condition (state) assessment, you will also explore the use of advanced estimators
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cement based on literature, theory, and experiments. Optimization of composition with other waste materials. Thermodynamic modelling and experiments with advanced technologies are used. Development of a