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Field
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, adaptive control strategies, and hybrid energy storage solutions to address key challenges in self-powered systems under dynamic environmental conditions by: Develop machine learning or heuristic-based
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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the research project This project is set to explore so-called shared control between the driver of a car and the car's safety systems. By mechanically disconnecting the driver's steering wheel from
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having passed exams in areas relevant to the subjects of image analysis and machine learning with a minimum of 90 higher education credits. Relevant courses include, for example, image processing, computer
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. Applying machine learning to New Zealand’s landslide inventories to model landslide location, character and dynamics. Integrating time-series and inventory data to develop new models to predict location
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analysis of large data sets, statistical modeling, and knowledge of at least one programming language (e. g.: R, Python and/or Julia) are required. Experience in machine learning and image recognition
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on AI-enhanced automation of transmission electron microscopy imaging and spectra acquisition, developing AI protocols based on ML, DL and computer vision within the project EDISON. Main Tasks and
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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analysis, computational text and image analysis, or machine learning. Scientific outputs within the subject areas of IAS research. Fluency in English is required. Knowledge of Swedish is also desirable
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and hands-on experience with AI and computer vision. Solid programming skills in Python, especially with PyTorch. Practical experience with deep learning projects, including working with attention