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for Industrial Mechanics is located at SDU's beautiful campus in Sønderborg, in the inspiring building of Alsion, which is a center for science, culture and learning in the heart of Sønderborg and perched
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focused on applying quantitative tools (e.g., statistics, machine learning, optimization, simulation) to healthcare delivery, healthcare operations and healthcare management as well as medical decision
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often related to domesticated species and humans, but increasingly also on other organisms. Our focus areas include quantitative genetics, deep learning, machine learning, population genetics, integrative
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specialized areas: Machine Learning / Deep Learning Uncertainty Quantification Wind Farm Flow Modelling Wind Farm Control Wind Farm Design Wind Farm Control Co-design Hybrid Power Plant Design & Control Co
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Engineering. Therefore, the department invites applications from candidates who are driven by excellence in research and teaching as well as external collaboration on societal challenges. The position will be
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handling robot, and running our Illumina NovaSeq instrument and other instrumentsrelated to NGS e.g., qPCR machine and Fragment Analyzer. In addition, you will be involved in supporting the research projects
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, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted
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candidates with expertise in one or more of the following specialized areas: Machine Learning / Deep Learning Uncertainty Quantification Wind Farm Flow Modelling Wind Farm Control Wind Farm Design Wind Farm
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, including bias mitigation and reinforcement learning techniques. Proficiency in Python and standard NLP libraries (e.g., Hugging Face and PyTorch). CHC is a research and development unit at Aarhus University
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, artificial intelligence (AI), machine learning, and computation have emerged as powerful digital technologies for creatively generating new design ideas and rapidly advancing formgiving methods within