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decision support tools by integrating health data, domain knowledge, and machine learning. Key objectives include improving diagnosis, monitoring, and treatment through early detection of deterioration
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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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experience with software licenses and computer hardware, including server setup Strong organizational skills and attention to detail Familiarity with GDPR and data security legislation Excellent written and
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methodologies in machine learning and causal inference applied to human health. Read more about NCRR here . Your job responsibility With a motivated, interdisciplinary team of approximately 70 researchers and
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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