83 molecular-modeling-or-molecular-dynamic-simulation PhD positions at Technical University of Denmark
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decentralized (building-integrated) scenarios based on technical and economic criteria. Work in the EU Horizon Europe funded TREASURE project (www.treasure-project.eu) focusing on simulation models and the
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SQL databases and file repositories. We are now taking the next strategic step: developing ontologies and a dynamic knowledge graph to semantically link our internal data systems - and connect them
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, at the DTU department of Applied Mathematics and Computer Science, section for Dynamical Systems. Furthermore, this is an Alliance PhD position, where you will have the privilege to benefit from two
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renowned research group for Gut, Microbes, and Health at the National Food institute, Technical University of Denmark (DTU). We offer a dynamic and sociable research environment with exiting challenges and
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collaboration that covers all aspects of our research: theory and modeling, sample growth and fabrication, experiments and demonstrations. We have created a dynamic research environment of young and senior
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to mechanical forces. We work with leading international groups on modeling and also conduct simulations at DTU. Our overarching goal is to understand and predict the mechanical behavior of metals during plastic
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cutting in the production facility. Establish a numerical model to simulate the glass cutting process. Design experimental measurements and assist in the integration of sensors in production. Acquire
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qualifications we are looking for: Excellent knowledge and practical experience on current molecular microbiology methods Experience with genomic and transcriptomics data analysis is beneficial. Experience with
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characterization aspect of the project, i.e. investigation of dynamics during catalyst activation and reaction by in-situ transmission electron microscopy. VISION is pioneering technology for visualizing catalytic
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than RGB will be actively researched. Exploring 3D canopy modelling and plant growth dynamics for digital twin integration. Self-supervised learning will generate multi-modal agricultural pre-trained AI