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PhD scholarship in Corrosion Mechanisms of Power Semiconductor Device and Components - DTU Construct
thermal energy systems. The research under the section of Materials and Surface Engineering is multi-disciplinary and covers materials science, chemistry, physics, solid mechanics, and manufacturing
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. Developing a computer model (“digital twin”) that link physical experiments with advanced control and operation strategies. The model will be utilized for techno-economic investigations regarding flexible
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therefore increase the energy efficiency of the process. This PhD project aims to get an in-depth analysis of the potential of coupled photonic resonant circuits for optical computing. The project will
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applied research, along with a thorough understanding of the process involved in transitioning from basic to pre-clinical research. Research training periods in another consortium member's lab lasting from
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compounds from seaweed. The goal is to advance sustainable separation technologies through green solvent design and process intensification. As part of the Alliance PhD programme, you will also spend 3–6
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between process/system performance, reliability, energy and resource efficiency. This position offers unique opportunities with respect to high level research, training and innovation within manufacturing
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machine learning algorithms/data science methods for clinical proteomics data. Further, during the enrollment process, you will define together with your supervisors (main and co-supervisor) additional
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equivalent to a two-year master's degree in mathematics, physics, computer science, electrical engineering or closely related fields with a focus on computer vision, machine learning or robotics A strong
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, chemical engineering, physics, materials science, or another relevant field. They demonstrate enthusiasm, creativity, persistence, and a sharp eye for detail. A collaborative mindset and the ability
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-film solar cells.” You will become part of an enthusiastic team working closely with collaborators at DTU Physics and DTU Nanolab to advance neural network-based methods for materials discovery. Project