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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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optimization frameworks that adopt an interdisciplinary approach, integrating concepts from operations research, transport modeling, welfare economics, transport justice and machine learning. You will be based
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. Developing a computer model (“digital twin”) that link physical experiments with advanced control and operation strategies. The model will be utilized for techno-economic investigations regarding flexible
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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doctoral candidate who meets the following requirements: A background and strong interest in aluminum alloys, fatigue analysis, and numerical modelling is preferred. Experience with computer aided design and
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an extrusion machine that produces large-scale earth blocks Building a 3D printer that utilizes earth materials for construction purposes Developing numerical process models that simulate 3D earth printing
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a highly interdisplinary projects where the PhD student will learn and develop skills in sustainable material design, membrane technologies, prospective life cycle assessment, and industrial
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materials discovery, materials processing, and structural analyses. We also focus on educating engineering students at all levels, ranging from BSc, MSc, PhD to lifelong learning students. We have about 300
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for the development of next-generation CAR T cell therapy for solid tumours. You will work with a wide range of methods, including molecular biology, culture of human T cells, CRISPR multiplexed genome engineering
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to learn A background in renewable energy, e-mobility, or engineering Good programming skills (e.g., Python, MATLAB or R) Familiarity with control concepts Ability to work effectively with data and applied