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tools (e.g., statistics, machine learning, optimisation, simulation) to healthcare delivery, healthcare operations and healthcare management as well as medical decision support. Your role is to build
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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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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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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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. These include, but not limited to: Research Question 1: How can multimodal UAV data (RGB, thermal, LiDAR, hyperspectral) be fused using machine learning to predict complex canopy traits such as water-use
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threats, and safe NLP models, contributing to a safe and secure society. Using insights from Cybersecurity to improve systematic security in NLP models. The candidate should have an MSc. in Machine Learning
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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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, 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