33 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" uni jobs at Aalborg University in Denmark
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on working in Denmark and at AAU at https://www.en.hr.aau.dk/information-and-guidance Salary and terms of employment The employment is in accordance with the Ministerial Order on the Appointment of Academic
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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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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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Disentanglement of the Exceptional Biological Learning Machine, which is headed by Professor Jan Østergaard. The goal is to develop novel information-theoretic methods for identifying and analyzing temporal and
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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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areas: cyber security privacy engineering cryptography and applied cryptography computer engineering edge or cloud computing and networking. You will be part of one of the department’s research groups in
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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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for collaborative learning will be open for appointment as 1st of July 2026. The employment will be a fixed term (3 years), full-time position, which will include both research and teaching activities. Your work
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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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-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