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in fluid dynamics, turbulence modeling, CFD, and turbomachinery. Experience with CAD and CFD tools (e.g., Ansys Fluent, CFX, StarCCM+, OpenFOAM). Programming skills (e.g., Python, MATLAB). Knowledge
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of biogeochemical processes with an emphasis on terrestrial ecosystems Development of observational techniques to monitor and assess biogeochemical feedbacks in the Earth system Theory and model development
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are seeking a highly motivated PhD candidate to develop efficient on-device generative AI systems based on large language models (LLMs). The project focuses on creating compact, low-latency, and energy
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the field and greenhouse. You have experience using modern approaches in root phenotyping, image analysis, or simulation modeling to understand the functional roles of plant roots in agroecosystems. You have
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section. The PhD-position's main objective is to qualify for work in research positions. You will report to the head of Department. Duties of the position Developing numerical models to simulate the thermo
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results with AI models and system simulations to create a digital twin of the PtX process for predictive optimization and scenario analysis. Funding This PhD position is generously funded through the Villum
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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actors involved in managing public space? How can they adapt to dynamic societal needs and increasing spatial interdependencies? What institutional models promote more efficient, equitable and resilient
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of cities. Develop the first national land use–transport interaction (LUTI) model for Australia. Evaluate policy scenarios involving HSR to realign population growth with sustainability goals. The selected
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performance limit of Ga2O3 power devices through finite element modelling (electrical and thermal) and device fabrication aimed at both power electronics and photovoltaics. A self-motivated individual who will