67 postdoctoral-coastal-modelling PhD positions at Technical University of Denmark in Denmark
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. Work will also involve electrochemical modelling using existing models and using AI based tools. The focus of the work will be to cater to the needs to high voltage/power in power electronic systems
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and what they mean for the system’s efficiency and safety. You will develop models of AI bidding strategies, analyze strategic interactions using game theory, and design optimization methods to identify
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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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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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both technical insight into data modeling and a solid understanding of how real-world engineering data is generated, structured, and used. We are seeking motivated candidates with strong programming
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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
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, 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