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for the position. Knowledge of TSN or real-time networking. Experience with one or more of: combinatorial optimization algorithms, ML algorithms, timing analysis network calculus, real-time systems. Excellent
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carriers within defects. The charge transport will be implemented stochastically to mimic nature. A significant focus of the project will be to apply machine learning techniques to optimize the model and
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primary role will be to contribute to project tasks that demand a deep understanding of electrical or thermal energy system operation and dynamics, modelling approaches, and optimization methods. To excel
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water quality. You will be embedded within the Ethio-Nature project, funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use
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the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use of machine learning and remote sensing to site nature-based solutions that enhance local
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, holistic planning and scheduling to optimize resilience across several factory systems. Development of AI-based tools and complex simulation systems is expected to be a core contribution. 3) IT and OT
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industrial and academic partners. The overall goal of the project is to optimize the design of water eletrolyzers for efficient green energy production. You will be conducting Computational Fluid Dynamic (CFD
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industrial and academic partners. The overall goal of the project is to optimize the design of water eletrolyzers for efficient green energy production. You will be conducting Computational Fluid Dynamic (CFD
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implement optimal experimental approaches for molecular scale quantum sensing Co-supervise and guide younger project students at the BSc, MSc and PhD level Maintain a thriving laboratory culture, ensuring
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CO2 capture from the atmosphere. Your objectives will include to: Develop new optimization and/or machine-learning based reconstruction and segmentation algorithms to improve image quality in time