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for this PhD proposal should have the following qualifications: M.Sc. degree in electrical engineering, mathematical engineering, acoustics, machine learning or similar; Solid mathematical and analytical skills
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vision, XR and generative models, specifically for capturing challenging scenarios and training deep learning systems to create better experiences for human users and learners. You will contribute
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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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at bachelor and master level, supervising project and thesis work in alignment with AAU’s problem based learning model, and, when relevant, co supervising PhD students. Under the guidance of more experienced
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systems with digital technologies from a socio-technical perspective. This includes human–machine interaction, XR-based interfaces, and engineering solutions for hybrid production systems. Candidates should
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have: A relevant PhD degree (e.g., NLP, AI, ML, Security, Cryptography, or a related field) A relevant MSc degree (e.g., Computer Science, Software Engineering, Machine Learning, Artificial Intelligence
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research in Time Series Analysis and Econometrics with focus on one or more of the following key research areas: Nonparametric estimation. Machine learning methods in econometrics and time series analysis
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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