44 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" uni jobs at Aalborg University
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, Denmark [map ] Subject Areas: Nonparametric estimation, Machine learning methods in econometrics and time series analysis, Statistics for high-dimensional data, Stochastic volatility models Appl Deadline
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project and process management, information management and data-driven approaches in the built environment. You will join a dynamic, interdisciplinary research environment and contribute to activities
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optimization of production systems and supply chains, including digital twins, virtual system validation, process modeling, and data-integrated decision models. Research should explicitly support managerial
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experimental methods such as viromics and metatranscriptomics. The data will be linked to soil and emission data to help create predictive models. Within a broader framework, your work will contribute
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) Data-driven and AI-assisted methods for power electronics Across the above areas, you are expected to contribute to model-based and data-driven/AI-based methods, including digital twins, physics-informed
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more about the department at www.es.aau.dk. Your work tasks The PhD project is part of a bigger Novo Nordisk Foundation (NNF) New Exploratory Research and Discovery grant entitled: Information Theoretic
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-drive systems. Across the above areas, you are expected to contribute to model-based and data-driven/AI-based methods, including digital twins, physics-informed learning, data analytics, and AI-assisted
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quantitative analysis of register, survey, and patent data to various qualitative methods. If you have an interest in, or experience with, novel computational methods such as NLP, machine learning, and AI
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testing and condition monitoring using modern machine learning, including multimodal foundation models and related data-driven and physics-informed approaches. Research topics may include visual and real
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hardware-in-the-loop testing of integrated energy systems. The candidate is expected to have a solid understanding of system monitoring, experimental data management, and validation of thermal systems, as