204 machine-learning-"https:" "https:" "https:" "https:" "https:" positions in Sweden
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Join and help us to derive global forest biomass data from the European Space Agency’s Biomass satellite mission. If you have interests in remote sensing, machine learning and forests, this is the
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Description of the workplace The position will be placed at the division of Computer Vision and Machine Learning at the Centre for Mathematical Sciences. The Centre for Mathematical Sciences is an
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of working with motion capture, eye tracking, machine learning, or other advanced behavioral analyses or related research experiences. A consistently excellent academic track record is required, including
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degree in machine learning. The successful candidate will be supervised by professor Aristides Gionis (https://www.kth.se/profile/argioni/ ). The research team focuses on developing novel methods
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from talented and highly motivated candidates to pursue a PhD in machine learning at KTH, Sweden, and NTU, Singapore. This is a fully funded, joint doctoral position that will lead to a joint PhD degree
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of full-time. Your qualifications You have graduated at Master’s level in biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered
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through a model-driven approach, i.e. a combination of simulation- and data-driven methods and tools with data analysis and machine learning as an important part. The work builds on established theories and
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different backgrounds. This position requires that you have graduated at Master’s level in in computer science, media technology, computer engineering, human-computer interaction, visual learning and
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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the Arctic, experimental tests of climate driven changes in carbon export from land and turnover and release of greenhouse gases (CO2 and CH4 ) from headwaters, and use of machine learning and process-based