66 parallel-computing-numerical-methods-"Prof" PhD positions at Technical University of Denmark
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acquisition and statistical methods is an advantage. Experience with numerical simulations (FEM). You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent
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enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment will be made by Assoc. Prof. Leticia Hosta-Rigau. We offer
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. Your work will focus on developing physics-informed AI methods to enhance decision-making in design and operation of next generation thermal energy storage systems, such as latent heat TES and
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quantum systems and quantum networking theory. Experience with numerical modeling of open photonic quantum systems. Experience with scientific computing using Python and/or Julia. Desired qualifications
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academia and industry. You will be involved in the “DTU Alliance” project in collaboration with Prof. Anna Scaglione at Cornell University, with the opportunity to undertake a research stay of 5–9 months
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in machine learning and artificial intelligence Experience with numerical analysis and scientific computing Knowledge of power systems and renewable energy technologies Experience in power system
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is expected to lead to the development of material evaluation methods and a structural monitoring framework to quantitatively assess the quality and robustness of concrete elements in infrastructure
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), robotics and computing, construction production processes, and life cycle and sustainability analysis (LCA). The successful candidate will be responsible for conducting cutting-edge research in the field
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for the efficient formation of high-value compounds. Advanced NMR methods and computational data analysis will be compounded to devise novel reactions towards pharmaceutical precursors, polymer building blocks and
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monitoring will be based on real time data streaming from the machine numerical control. The project will cover all the aspects related to the implementation and automation of the tool life cycle management