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satellite, airborne and ground-based measurements with modelling and machine learning, we collaborate globally to monitor environmental change and support a sustainable future. About the research project
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candidate will have the opportunity to collaborate closely with our Danish industrial partners on innovative use cases, including advanced condition-based monitoring in industrial settings and speech
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conceptual DFT (linear response function, Fukui functions) or QTAIM theory (delocalization index), and their validation on a set of compounds known from the literature - interfacing a MLIP (Machine-Learned
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focus on advancing human health. Our overarching mission is to define the molecular mechanisms of peripheral tolerance through in-depth immune monitoring. The laboratories utilize primary specimen from
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calculations, sample size determinations and statistical analysis plan. 3. Apply machine learning approaches to integrate diverse datasets collected from study participants to identify subtle differences
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industrial conditions. Keywords: Artificial intelligence, autonomy, digital twin, edge computing, UAV systems. Objectives: Support the development of AI and machine learning algorithms for autonomous
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the safety, functionality, and sustainability of civil infrastructure under both normal and extreme conditions. Research activities may include topics such as infrastructure monitoring, resilience assessment
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polar orbit, passing near the poles about 15 times per day and regularly observing the CIFAR study region. Its payload - two optical cameras, a thermal camera, and onboard machine-learning capabilities
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machine learning and operations research. b) The applicants are expected to deliver research outcomes to our industry partners, to support practical applications in semiconductor manufacturing. c
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motivated post-doctoral associate with a strong background in control systems and machine learning to join the research team of Prof. M. Umar B. Niazi. The position focuses on the development of digital twins