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characteristics are intricately linked to the electronic configurations of the f -element ions, and any modifications in these configurations result in dramatic changes in their physical and chemical properties
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integrating machine-learning techniques with experimental datasets on bioplastic degradability. You will work to establish links between polymer features and degradability through mapping of existing data
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linked data Sensors as part of Internet of Things (IoT) and integration of sensory information in simulation models as part of Digital Building Twins (DBT) during run-time Life cycle and sustainability
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cell factory development, data analysis and fermentation optimization. Oversee project planning, execution, and resource allocation. Provide teaching, scientific guidance and mentorship to PhD and Msc
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Job Description If you are passionate about marine technology, data analytics, and ecosystem research, we have an opportunity for you! DTU Aqua invites applications for a PhD position focused on
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the MSCA Doctoral Network CoDeF, with four of the PhD positions located in and around Copenhagen. More information on the CoDeF training network can be found here . Your main supervisor will be Professor
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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optimization of SERS substrates, Raman measurement, data analysis, and validation of results with reference methods such as high performance liquid chromatography (HPLC). You are expected to have a solid
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learning approaches and develop a theoretical understanding potentially based on differential geometry. In particular, deep neural networks perform surprisingly well on unseen data, a phenomenon known as