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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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Job Description The project takes place in the Quantum Light Sources group at DTU Electro, where we design, model, fabricate and test sources of single photons or entangled photon pairs
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and operation of building HVAC systems, these technologies support both energy efficiency and flexible demand objectives. Model predictive control (MPC), which involves physics-based building energy
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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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domains. The scientific outcomes are expected to be significant in: Earth system science – by improving models of Earth surface evolution and enabling better predictions of landscape response to climate
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microbial metabolites and its effect on chronic kidney disease and cardiovascular complications, using an in vivo model of chronic kidney disease. Responsibilities and qualifications As a PhD student, you
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market frameworks and business models for fair value distribution will be analysed. Responsibilities and qualifications Your primary research tasks will include: Develop and simulate coordinated control
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a team of 25 colleagues dealing with CCUS, and industrial partners in Denmark as well as abroad. Your primary tasks will be to: Apply AI in context of capture solvent modelling Understand and analyse