50 deep-sea-modeling Postdoctoral positions at Technical University of Denmark in Denmark
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in research and development of sustainable energy conversion technologies. We are recognized as global leaders in this field, supported by state-of-the-art facilities and deep expertise. Our
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, particular consideration will be given to: A strong interest and documented experience in developing and testing, at sea and in the lab, the capture performance of active and passive fishing gears. A strong
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strategies for conventional HVAC systems that condition entire indoor spaces using existing thermal comfort models, which could improve the performance of existing buildings and HVAC systems. The work will
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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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successful, it will contribute to environmentally responsible shutdown of oil & gas operations in the North Sea. As part of the project, you will develop as a researcher and deepen your technical skills
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modelling. It is expected that the degree of involvement in meetings and organisational tasks related to research projects will increase steadily during the employment. The following qualifications are highly
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will support the long-term goal of helping constructing reasoning techniques to democratize and render transparent legal processes. As a postdoc in BPM, you are familiar with the role of process models
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production and quality control will help save natural resources as well as reduce waste material and energy consumption. Formulation and test methods using mathematical modelling and prediction tools. Fouling
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-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis
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-based simulation model for assessing future mobility technologies in the Greater Copenhagen region. Explore the development of machine-learning based scenario discovery for future mobility policy design