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related to Riemann-Steltjes optimal control to combine PMP with Bayesian Optimisation, allowing for data-efficient learning. You will then implement and validate the new method on simulated fermentations
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environmental factors such as fluctuating wind speeds and saltwater exposure. Using advanced statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will
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Bayesian Networks (DBNs) for probabilistic risk modelling Scenario-based simulation for rare-event analysis You will be part of a dynamic, interdisciplinary research setting at one of Europe’s leading
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, programming, and computer simulations. The focus of the project (funded by the Danish Council for Independent Research) is to explore the interaction between optimally controlled coherent (laser) light fields
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digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design
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, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks
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employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning
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Job Description These days, the inner workings of molecules and materials can be probed and modelled by advanced simulation tools on modern computer architectures. However, the routine applications
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under uncertainty (e.g., planning, reinforcement learning, probabilistic reasoning); Multimodal information fusion and state estimation; Foundational or representation learning models for robot
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stakeholders, including purchasing decision makers Support mapping of the pathway to market, including customer segmentation and potential market size estimation Contribute to analysis of reimbursement pathways