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Description of the offer : At the Technical University of Denmark, Department of Energy Conversion and Storage (DTU Energy) we are looking for a PhD-student to conduct research on modeling of ideal
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Job Description If you are establishing your career as a scientist and you are looking for the best possible foundation for fulfilling your dreams and ambitions, it is right here in front of you. We are looking for a passionate PhD candidate in Thermal Energy Systems with strong programming,...
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modeling paradigms; tool implementation and application to complex cyber-physical systems (e.g. transport, water, energy) Requirements Applicants must have a master’s degree in computer science or a closely
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friendly catalysts and substrates. We use NMR spectroscopy for serendipitous discoveries and unbiased characterizations of molecules, their conversion, and their interactions in complex systems. Solvent
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with colleagues at DTU and IIT Bombay, as well as with academic and industrial partners globally. The main purpose of this PhD position is to develop, implement and assess machine learning models
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system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models and machine
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two-dimensional active sites, however, have a fundamentally limited efficiency for facilitating complex reactions that involve three or more reaction intermediates, due to the "scaling relations". In
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components are in use. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process
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bottlenecks in data and system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models
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wearable and ambient IoT sensing systems for activity and health monitoring. Implementing embedded AI models for anomaly detection and behaviour analysis. Working on digital twin and serverless IoT