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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 Are you driven by the potential of AI in enzyme engineering? Do you want to design novel enzymes from scratch using cutting-edge generative models and validate their function in
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-world medical applications? Do you want to design next-generation protein therapeutics using cutting-edge generative models and validate them in the lab? Join a collaborative PhD project at DTU
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components are in use. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process
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the carbon footprint of milk production. The project will apply advanced statistical methods, artificial intelligence, and cutting-edge genetic models to support and enhance management and breeding decision
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conversion reactions. The second position is focused on modelling stability of electrocatalyst materials. The aim is to develop a framework to predict metastability of catalyst materials. Among the methods
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proficiency in relevant programming languages (e.g., Python, C++) and tools such as ROS. Experience in simulation and digital twins, as well as the use of synthetic data for training machine learning models, is
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tasks will be to: Develop and implement machine learning models for dynamic simulations of renewable power systems Develop comprehensive guidelines for verifying and testing dynamic equivalents Integrate
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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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decentralized (building-integrated) scenarios based on technical and economic criteria. Work in the EU Horizon Europe funded TREASURE project (www.treasure-project.eu) focusing on simulation models and the