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The fellowship period is three years. The PhD candidate will work on the project entitled "Male Reproductive Disorders of Foetal origin – Development of an Organoid Model for Mechanistic Insight". Foetal life is a
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). Applicants must have experience in one or more of the topics: Model-predictive control Numerical optimization Econometrics Virtual power plants Power systems and/or power electronics Machine learning Renewable
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a check versus national export, sanctions and security regulations. Candidates may be excluded based on these checks. Primary checkpoints are the Export Control regulation, the Sanctions regulation
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Centre for Knowledge-driven Machine Learning is a Centre of Excellence, funded by the Research Council of Norway. Researchers at Integreat develop theories, methods, models, and algorithms that integrate
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Excellence, funded by the Research Council of Norway. Researchers at Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining
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) , Department of Biostatistics, Institute of Basic Medical Sciences (IMB), University of Oslo (UiO), Norway. The candidate shall take part in the research group on “Statistical models for high-dimensional and
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systems such as fuel cells and batteries. Intelligent control and management systems will be designed to enhance efficiency, sustainability, and seamless integration. Developed models and strategies will be
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to candidates with a multi-disciplinary background consisting of both control theory (or related fields) and economics (or related fields). Applicants must have experience in one or more of the topics: Model
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of Biostatistics, Institute of Basic Medical Sciences (IMB), University of Oslo (UiO), Norway. The candidate shall take part in the research group on “Statistical models for high-dimensional and functional data
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and environment (air, soil, and groundwater). A schedule optimization problem has emerged in this context, where tasks correspond to contaminated sites to be cleaned, while managing risks that evolve