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environment toits 38,000 students (FTEs) and 8,300 employees, and has an annual revenues of EUR 935 million. Learn more atwww.international.au.dk/
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engineering students at all levels, ranging from BSc, MSc, PhD to lifelong learning students. We have about 300 dedicated employees. Read more about us at www.energy.dtu.dk . Technology for people DTU
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not a requirement, but if the postdoc is expected to teach, then documented teaching experience can be included in the overall assessment. Application Applications must be submitted online in PDF
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motivated researcher with: Strong background in control and optimization, preferably with experience in model predictive control (MPC). Solid skills in machine learning algorithms and data analysis
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. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power electronics, and self
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on post-training methods for these low-resourced languages, for example, by investigating the role of synthetic data, among other data augmentation techniques, and the role of in-context learning in
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provide assistance in organising workshops and advisory board meetings. The post is for two years and represents an exciting opportunity to acquire valuable research experience, to contribute
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iNANO at Aarhus University is seeking a postdoctoral fellow for the Novo Nordisk Foundation CO2 R...
(FTEs) and 8,000 employees, and has an annual revenues of EUR 885 million. Learn more at www.international.au.dk . Application procedure Shortlisting is used. This means that after the deadline
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Postdoc in development and testing of electrodes for liquid alkaline water electrolysis - DTU Energy
to lifelong learning students. We have about 300 dedicated employees. Read more about us at www.energy.dtu.dk . Technology for people DTU develops technology for people. With our international elite research
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-based simulation model for assessing future mobility technologies in the Greater Copenhagen region. Explore the development of machine-learning based scenario discovery for future mobility policy design