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Fellow in Deep learning for subsurface imaging Apply for this job See advertisement About the position Position as Postdoctoral Research Fellow in Deep learning for subsurface imaging available
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deep learning methods for multi-modal image data applied to industrial challenges in the energy sector? Advance the use of seismic tiles for extracting information about geological layers? Contribute
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JavaScript for all functions to work properly. Please turn on JavaScript in your browser and try again. UiO/Anders Lien 1st November 2025 Languages English English English PhD Research Fellow in Deep learning
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learning in the Visual Intelligence Graduate School . You are keen on contributing to new advances in deep learning methodology for earth observation. You will work on deep learning methods for detection
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with the centre’s user partner Kongsberg Satellite Services (KSAT). We are therefore seeking someone with a strong interest and competence in deep learning. Working environment: The project will be done
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. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites. We seek a candidate with a strong background in one or more of the fields of rock physics
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, computer science, AI/ML, medicine, statistical genetics, psychology, molecular genetics, or a related field. Demonstrated expertise in machine learning, deep learning, or advanced statistical analysis applied
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genetics, or a related field. Demonstrated expertise in machine learning, deep learning, or advanced statistical analysis applied to complex, multimodal data. Hands-on experience with high-performance
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develop new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. General information about the position. The position is a fixed
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/ deep learning workflows) and integrate these readouts with viability metrics and genomic data. Collaborate closely with clinicians, surgeons and SINTEF partners on patient sample handling, experimental