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renewable power system dynamics. Collaborate with experts from DTU and IIT Bombay, gain hands-on experience with advanced simulation tools, and contribute to high-impact publications. Enhance your
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characterization aspect of the project, i.e. investigation of dynamics during catalyst activation and reaction by in-situ transmission electron microscopy. VISION is pioneering technology for visualizing catalytic
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renowned research group for Gut, Microbes, and Health at the National Food institute, Technical University of Denmark (DTU). We offer a dynamic and sociable research environment with exiting challenges and
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projects partners. This requires good communications skills but also allows you to co-operate with leading European research institutes. Responsibilities and qualifications You will be part of a dynamic work
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Job Description You will join a supportive and dynamic research team working at the intersection of machine learning and operations research. Your main task will be to design and implement ML
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to the high-temperature PEMFC to produce warm water for practical applications (e.g., heating and washing) in disaster areas. Investigate the thermal dynamics and overall performance of the HT-PEMFC stack and system under
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PhD Position in Hydrogen/Deuterium Exchange Mass Spectrometry to Study the Regulation of Lipoprot...
part of an international and dynamic research team. You will be motivated to use advanced HDX-MS to investigate the regulation of lipoprotein lipase. You will manage an independent research project and
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, ambitious, dynamic, and well-resourced working environment. Our unit and our department have state-of-the-art facilities for flow cytometry, next generation sequencing, single-cell transcriptomics, mass
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limitations of current EMG-based techniques, particularly in dynamic movement tasks. The PhD student will focus on the design, development, and validation of the HD-sEMG/ultrasound system. This includes
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than RGB will be actively researched. Exploring 3D canopy modelling and plant growth dynamics for digital twin integration. Self-supervised learning will generate multi-modal agricultural pre-trained AI