61 digital-image-processing-phd-scholarship Postdoctoral positions at Technical University of Denmark in Denmark
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internationally recognised academic environment with over 400 employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering
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recognised academic environment with over 400 employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including
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technological solutions. DTU Health Tech’s expertise can be described through five overall research areas: Diagnostic Imaging, Digital Health, Personalised Therapy, Precision Diagnostics, and Sensory and Neural
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synergy with another postdoc working on the same project, whose focus is on the development, demonstration and application of the functionalized quartz resonators integrated into a sensor prototype. If
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for society. DTU Health Techs expertise spans from imaging and biosensor techniques, across digital health and biological modelling, to biopharma technologies. The department has a scientific staff of about 210
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recruitment and supervision of future PhD students and postdocs. We seek applicants who: Hold a PhD in molecular biology, biotechnology, bioengineering, or related fields. Demonstrate enthusiasm for complex
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skills and to be fluent in English. Prior research experience in clay processing is preferred. As a formal qualification, you must hold a PhD degree (or equivalent). We offer DTU is a leading technical
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for companies to develop new and innovative services and products which benefit people and create value for society. DTU Health Techs expertise spans from imaging and biosensor techniques, across digital health
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at the intersection of digital building technologies, data science, and energy systems. By joining our forward-thinking section Digital Building Technologies at DTU Construct, you will gain hands-on experience in
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digital co-simulation platforms (e.g., Modelica-Python/Simulink) Applying machine learning and data-driven approaches to enhance the operation of district heating substations Participating in course