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to engage in pioneering research, collaborate with a large, dynamic and multidisciplinary team, and advance the field of quantum computing through innovative algorithms and technologies. This is an exciting
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maintaining the radioisotope license. Educate technical personnel and students in PET/SPECT/CT imaging and radiation safety. Guide researchers in experimental design, data interpretation, and imaging-related
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optimization, for enhancing light trapping in nanostructured thin-film solar cells. Your role will focus on developing and applying large-scale electromagnetic simulations to identify optimal nanostructured
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group specializes in experimental quantum technologies spanning quantum information processing, quantum communication, and precision sensing. In this PhD project, you will work at the forefront of solid
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recent large-scale capabilities in physics. Reliability, exploring uncertainty quantification and robust inference in machine learning. Explainability, leveraging identifiability and unique recovery
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the potential of biological systems. Big data approaches and analysis of biological systems are key research instruments at the Center. DTU Biosustain utilizes these advances for microbial cell factory design to
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robust models – and for clinicians, whose goal is to determine when to trust the models. We therefore seek candidates who have strong technical background in working with large-scale deep learning models
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solid background in analytical chemistry, spectroscopy, medical devices, sensors, bioanalysis, and if possible large data analysis. You are highly independent, self-motivated and enjoy collaboration in a
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modern versions of OpenGL and GLSL Experience with databases and/or IoT protocols / Big Data In addition, it is expected that you fulfill the following: work independently and to efficiently deliver
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universities, and three research and technology organizations. This PhD project is focused on glass cutting, where large, laminated glass panels are cut to sizes for window production. The process involves pre