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estimation methods or Kalman Filters is advantageous. Ability to work independently and take initiative in planning and executing computational research. Creative, innovative and driven by scientific curiosity
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, ethnography, and policy studies. Further description of the project can be found here: https://dff.dk/en/our-funded-projects/meet-the-researchers/research-leaders/eksterne-personer-en/research-leaders-2026
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applications for a three-year PhD Stipend from October 1st or soon hereafter in dynamic modelling, simulation and optimization of Power-to-X processes for renewable fuel production. The project is primarily
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. AEGIS sits where two fast-growing fields meet: datacentres and AI infrastructure on one side, energy and power systems on the other. Few people today can work across both, so the skills you build here
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. Please find and follow guidelines and further information on PhD scholarships and the PhD Program at https://www.cbs.dk/en/research/phd-programme Details about CBS and the Department of Marketing
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environment. Our research facilities include access to state of the art pilot plants, ability to use a range of sensors and expertise in process modelling and design. The PhD project will be undertaken in
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is the development of hybrid models that combine our integrated national model of Denmark with AI-based methods to improve predictions of floods and droughts. We seek to enhance our ability to predict
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the ability to learn methods rigorously and use them to answer substantive questions. The candidate should be able to plan and execute extended fieldwork independently, work across two very different
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with Git and modern deep learning frameworks (PyTorch/Jax/TensorFlow) Ability to work independently and in collaboration with researchers, engineers, and domain experts. Strong motivation to conduct
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into frontrunner firms in the Danish fashion and textile industry (see https://vbn.aau.dk/da/publications/grøn-omstilling-i-den-danske-mode-og-tekstilindustri-virksomhedsb/ — available in Danish only), the premise