24 data "https:" "https:" "https:" "https:" "https:" "https:" "CNRS" Postdoctoral positions at Aalborg University
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Are you interested in working with sensitive health data and making a real impact on how it can be used safely and responsibly? At the Center for Clinical Data Science (CLINDA), Department
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dissemination is expected to focus on leading Human-Computer Interaction venues. For further information about the project, see: https://dff.dk/en/our-funded-projects/meet-the-researchers/research-leaders
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colleagues in the group and external partners in Denmark and abroad. You will work with the design and execution of research studies, the analysis and interpretation of data, and the dissemination of results
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colleagues in the group and external partners in Denmark and Europe. You will work with the design and execution of research studies, the analysis and interpretation of data, and the dissemination of results
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and external partners in Denmark and abroad. You will work with the design and execution test campaigns, the analysis and interpretation of data, and the dissemination of results in high-quality
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in general. You are very well-versed working with data, models, statistics, simulations, and in general quantitative methods. Basic experience with programming (e.g. python) is a requirement, while
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within this field. Your work tasks In this position you will conduct research within Computer Vision and Deep Learning, with a particular focus on the development of an AI-powered framework
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information at our website http://www.cnap.hst.aau.dk If you have any questions about the position, you are more than welcome to contact us. You will find contact persons at the bottom of the jobpost. Further
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innovative ways to utilize the many data sources already available in PREDICT, including registry data, genomics, microbiomics, metabolomics and epigenetics. The postdoc fellow will join a multidisciplinary
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benchmark the developed framework using project data, high-fidelity simulations including hardware in the loop, and relevant industrial case studies, assessing its robustness and computational performance