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Field
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research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which
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) structural and cultural determinants of well-being and mental health in national and global cohorts. The postdoctoral research fellow will contribute to multiple research projects by engaging in the following
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for supervisory risk control. A theoretical foundation for risk visualizations to human supervisors for both single and multiple autonomous systems. To contribute to synthesizing the research results in
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is also placed on your: has expertise in software tools related to simulation/ optimization of energy systems, and coding using a high-level language (e.g. Python) has experience in analyzing multi
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years and in the relevant areas of Machine Learning / Artificial Intelligence, Credit Risk Modeling and Operations Optimization Modeling; The candidate must have strong programming skills in Python, and
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policy relevance. Coordinate modelling activities across multiple projects and deliver high-quality outputs on time. Integrate new methodologies, including AI and machine-learning approaches
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and data analysis tools commonly used in bioinformatics (e.g., shell scripting, Python, R). Solid understanding of omics data, including metagenomics, RNA-seq, and metabolomics. Experience with
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your points for admission. Emphasis is also placed on your: strong background in quantitative analysis and data manipulation proficiency with programming languages such as R, Python, MATLAB, or similar
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foundation for risk visualizations to human supervisors for both single and multiple autonomous systems. To contribute to synthesizing the research results in the project into a coherent framework. The
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. Researching and developing novel machine learning architectures for integration across multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and