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application! We are now looking for a PhD student in Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Your task will be to analyse and adapt vision
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new generation of data-driven methods for understanding biological form, function, and evolution. The project combines computer vision, machine learning, genomics, and biomechanics, and involves large
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vision, machine learning, deep learning, bioinformatics, advanced microscopy, cell biology, or RNA biology. Education in mathematical statistics. Experience in deep learning, computer vision, or neural
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methods for understanding biological form, function, and evolution. The project combines computer vision, machine learning, genomics, and biomechanics, and involves large-scale multimodal datasets including
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physics, electrical engineering, image processing, computer vision, AI, machine learning, data science, computer science, applied mathematics, or in a similar field, or have completed at least 240 credits
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the national Data-Driven Life Science (DDLS) program. About the position and the project As an industrial PhD student, you will be employed by the startup company PredictMe AB while being formally enrolled as a
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processing, computer vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. The University may permit
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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta