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of a novel condition monitoring system. Responsibilities: Develop machine learning algorithms and methods for condition monitoring of a disruptive new main bearing technology. Analyze sensor data from
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functional safety, security, quantitative performance, and exploitation of modern machine learning technology. It is the overall hypothesis of S4OS that a full integration of model checking and synthesis with
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is founded by Innovation Found Denmark. Responsibilities: In the project two main approaches are compared. One based on black/gray box machine learning methods and another one on gray/white box data
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. To overcome this challenge, neural architecture search and other ideas within the general field of automated machine learning have been proposed. We seek one or more PhD students(employed as PhD fellow if you
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environmental and heritable factors in a family tree of over 6 million Danes affect disease risk, to using machine learning on genetic and phenotypic data to define clusters of patients with different disease
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In line with the commitment to building a theoretical foundation of safe reinforcement learning, we are looking for a highly motivated and talented PhD student to join our team. The position is a
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of AIS data to trajectories for further analyses. The open position is targeted towards ensuring the validity of the AIS data at multiple level. For this task, we envision developing machine-learning
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Postdoc in statistics to develop Bayesian privacy metrics for synthetic health data (2024-224-05725)
one or more of these additional criteria: Synthetic data generation Machine learning Large health register data GDPR compliance rules Python Linux Valued personal competencies are: Independent and
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requires internationally recognized research experience within one or more of the following areas: communication networks control systems sound machine learning signal processing robotics antennas The main