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background in Intelligence, Security, or a similar degree with an academic level equivalent to a two-year master's degree and with an interest (and ideally some experience) in Agent-based Modelling, Simulation
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the development of a Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and
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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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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
feedback, and real-time agent-based simulation for guiding optimal work performance. Following smart serious gaming approaches, novel artificial intelligence forecasts human behavior and provides active
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of the nonlinear structural performance utilizing reliable and computationally efficient numerical structural models. To support the condition (state) assessment, you will also explore the use of advanced estimators
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offered in this context, with the objective of modelling, coding, and field-validating a new mechanistic analysis tool for pavements containing fungal-bound granular layers. The research will focus on urban
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industrial processes. Your research will drive a paradigm shift in how TES systems are modelled, integrated, and controlled within industrial settings. You will develop novel, adaptive, physics-informed models
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and colleagues working with theory that will complement your work with density functional theory, phase-field simulations, and finite element modelling. Qualified applicants must have: Enthusiasm
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an extrusion machine that produces large-scale earth blocks Building a 3D printer that utilizes earth materials for construction purposes Developing numerical process models that simulate 3D earth printing
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models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train these methods in a closed-loop