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DTU National Food Institute, at the Technical University of Denmark, is seeking highly motivated applicants for a Strategic Alliance PhD scholarship focused on the designing and modelling of switchable
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opportunities to participate in professional and personal development training. Through your work you will gain a unique skill set at the interface between modelling and prototyping of electrode materials
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on the development of AI models for analysis of cardiac CT scans, with the aim to explore how machine learning models can quantify cardiovascular disease and predict future events from CT scans. The project will
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typologies for hot and humid climate is scarce. This issue arises from the complexity of modeling these factors, which requires a co-simulation approach and validation of these. PhD position will focus
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students. In the project you will: Perform longitudinal proteomic and degradomic analyses of wound healing models and clinical samples. Identify proteolytic cleavage sites and post-translational
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leverage the power of field-programmable gate arrays (FPGA) to deploy machine learning models on the edge with low latency and high energy efficiency. This added intelligence will enable the rapid evaluation
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integrated with bioinformatics including large language models. The PhD scholarship includes an obligation for a 9 month research stay at the Chinese Academy of Sciences in Beijing, China integrated with
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well as emerging materials. These experimental techniques will be complemented by advanced electromagnetic modelling. Responsibilities and qualifications The ideal candidate will possess demonstrated expertise in
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. We are a team of currently about 20 researchers, engineers and PhD students, specialized in robot solutions involving modelling, simulation, and control of advanced robot technologies (systems and
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of “Developing locally integrated models for sustainable energy investments in rural areas”. The sustainable transition of Europe’s energy infrastructure involves large investments in new infrastructure, such as