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models to estimate, validate, and optimize the energy consumption of SQL queries for both research and industry use. The project involves partners including Aalborg University (AAU), Roskilde University
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to improve energy flexibility in our pilot cases Development of building simulation models supporting energy and indoor climate optimization Analysis of building performance data for occupancy detection and
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thermal and/or thermochemical energy storage systems. Implementing and validating advanced thermodynamic models for performance prediction and optimization. Collaborating with experimentalists and industry
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on realistic workloads and work on model readiness for integration into software quality assessment tools. Together, the partners will co-develop a catalogue of energy-aware SQL optimizations, ensuring
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for industrial decarbonization with emphasis in system development, modelling, optimization and validation, and focus on: Develop thermally integrated storage and conversion systems, including Carnot batteries and
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HVAC control practices and to develop innovative control strategies for improving comfort and energy performance of buildings and HVAC systems. This includes developing and testing optimized control
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team working at the interface between chemical engineering, data science, and semantic web technologies. The work will be tightly integrated with other digitalization activities at DTU KT
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coating production and coating use. Test methods Reliable and fast test methods to optimize coating performance is of utmost importance in coatings development. We work on new test methods and optimisation
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main campus. Depending on the position, the work will involve a combination of experimental research, process simulation, reactor operation, and system-level optimization. We are currently advertising
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advanced AI algorithms to optimize and understand the optical properties of light-trapping surfaces. (more information can be found in the following News post ). You will work closely with colleagues both