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Field
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the group's research on developing novel machine learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image
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of causal reasoning tools, including causal inference, counterfactual analysis, causal discovery. Development of deep learning methods on computer vision. Job Requirements: Preferably PhD in Computer
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novel research methodologies in computer vision, deep learning architectures, and neuro-fuzzy systems to contribute to the development of robust AI frameworks for medical diagnosis and treatment support
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learning research and development, particularly with a specialisation in such areas as: digital forensics, computer vision, biometrics (face or voice recognition, etc.) and natural language processing
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. These experiments will be repeated for a database of events covering different sea ice types, conditions, locations, and rates of ice deformation (from docile to violent). Machine learning techniques will then be
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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English PhD Research Fellow in Deep learning for imaging Apply for this job See advertisement About the position Position as PhD Research Fellow in machine learning available at Department for Informatics
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of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience of C/C++/C#, TensorFlow would be beneficial
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Observation, Renewable energy, Machine Learning, Complex Systems Modelling, Space Physics, and Ultrasound, Microwaves and Optics. The department provides education at the Bachelor, Master, and PhD levels. Want
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experience in the fields of HRI, robotics, computer vision, or machine learning. Programming skills. Contracting requirements: Presentation of the academic qualifications and/or diplomas, if applicable