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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
three years emphasizing the development and testing of next-generation serious gaming approaches with respect to construction safety, health, and well-being or emergency response applications. This PhD
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will autonomously estimate the flexibility of converter-interfaced loads, communicate with an aggregator, and modify their decentralized control to contribute to the resilience and reliability
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
PhD student eager to dedicate three years emphasizing the development and testing of next-generation serious gaming approaches with respect to construction safety, health, and well-being or emergency
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, and characterization of such devices. Responsibilities and qualifications The focus of this position is to help advance the development of a reliable and efficient dual-fuel HT-PEMFC using multi-physics
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to material, cutting tools and parts production. The PhD project will therefore focus on the development of an integrated system combining direct and indirect tool wear monitoring for reliable residual life
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technology, positioning your career for long-term success and global scientific impact. Your primary role will be to pioneer and optimize advanced electron-beam lithography techniques to demonstrate reliable
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Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience
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on humidity/corrosion reliability of electronics is also suitable. For all qualifications, prior experience of corrosion of electronics or similar topics is an advantage. A combination of knowledge in
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engineering challenge. Key to success will be the development of cost competitive and reliable methods to produce hydrogen via electrolysis of water/steam driven by green electricity. Hydrogen can be used as a
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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution