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of advanced modelling and machine learning methods, and may involve the following areas: Dimensionality reduction. Data-driven methods for estimating dynamical models Data-driven methods for estimating
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have been completed earlier. Additional requirements: Very good oral and written proficiency in English. Demonstrated experience in computational methods, particularly in deep learning and computer
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streams, contributing to elevated environmental mercury levels and increased human exposure. It is estimated that around 300 tonnes of mercury are released annually through these processes, making them one
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will be as a researcher in a two-year project carried out in close collaboration with our industry partner. The goal is to develop methods for an ML-based decision support system for monitoring and fault
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increased human exposure. It is estimated that around 300 tonnes of mercury are released annually through these processes, making them one of the top three sources of anthropogenic mercury emissions worldwide
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-year project carried out in close collaboration with our industry partner. The goal is to develop methods for an ML-based decision support system for monitoring and fault diagnosis of gas turbines
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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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and project developers in reusing them in new projects. Taking the Chalmers campus as a starting point, we are developing scalable, AI-powered methods, such as computer vision for street-view imagery
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communication networks using AI-powered methods. We will advance the research front in defending future generation networks by: prevention of cyberthreats through anticipating and mitigating them, accurate
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vision, animal welfare, and behavior. The position is aimed at a method-oriented researcher interested in developing and applying computer vision–based methods in real-world settings. The project is part