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? Would you like to contribute to the design of sustainable construction materials? And collaborate within an international consortium of architects, materials and textiles scientists, and engineers to help
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construction materials? And collaborate within an international consortium of architects, materials and textiles scientists, and engineers to help translate bio-inspired knowledge to the development of modular
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. The PhD candidate will be supervised by Dr. Kristin Kirchner (TU Delft and KTH Royal Institute of Technology Stockholm). For more information regarding Kristin Kirchner's research, please visit her webpage
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group in the Department of Chemistry and Chemical Engineering at Chalmers University of Technology to develop recyclable polymeric electrocatalysts. If you are motivated to design and synthesize
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topic in a vibrant research environment with excellent infrastructure. Information about the division In the division of Chemical Biology (ChemBio), we combine and develop protein engineering, synthetic
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) and fluid-structure interaction (FSI) with patient-data to provide insights into the development and progression of adverse outcomes associated with TAVI and valve-in-valve procedures, including
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to find and access and because databases are not systematically interoperable.Objectives:We seek a highly motivated and skilled Doctoral Candidate (DC) to join the Bioprocess and Biosystems Engineering
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International Centre for Translational Eye Research / Institute of Physical Chemistry, Polish Academy of Sciences | Poland | 5 days ago
effective surgical interventions, new therapies for eye diseases, and diagnostic methods to improve prognosis and restore vision. The main technology to be developed under this program will be a novel robotic
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) and fluid-structure interaction (FSI) with patient-data to provide insights into the development and progression of adverse outcomes associated with TAVI and valve-in-valve procedures, including
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. The need to predict the transition state structure as input for quantum chemical barrier predictions adds further complications. Machine learning (ML) models of quantum chemistry can achieve fast and