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Field
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to both contribute to new advances in a field with real world applications. Your research focus will be on challenges related to computer vision for geophysical image data. You will investigate and develop
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are template based and all US measurements are auto-populated into templates for increased accuracy and efficiency. Fourteen fellowship-trained, subspecialized expert faculty perform both image interpretation
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biology, engineering, biophysics, computer science) and experience in 3D imaging of brain tissue, image segmentation, and handling large datasets. You are comfortable with machine learning and image
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Singapore, Indonesia, and the broader Southeast Asian region. Experience with machine learning or data-driven approaches for subsurface imaging or hazard assessment (preferred). We regret to inform that only
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INESC TEC is accepting applications to award 4 Scientific Research Grant - NEXUS - CTM (AE2025-0564)
of computer networks, namely the various protocol layers of the OSI model; - Knowledge of IEEE 802.11 networks and narrowband HF links.; ; Research Topic #3 – Video Transmission using Semantic Communications
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, and formulation of clinical study design, image processing, machine learning, and statistical analyses to illuminate specific research questions. Among the machine learning techniques, deep learning
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Equinor. The position is in the Digital Signal Processing and Image Analysis (DSB) research group, Section for Machine Learning, Department of Informatics. For more information about the position, see https
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are template based and all US measurements are auto-populated into templates for increased accuracy and efficiency. Fourteen fellowship-trained, subspecialized expert faculty perform both image interpretation
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data (such as imaging datasets, electrophysiology datasets or video data) is highly desirable. For NeuroAI focus: Proficiency in popular deep learning, computer vision and/or user-interface software
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approaches would be a plus. Qualifications Candidates must have a passionate enthusiasm for research, a strong background in at least one of the following fields: signal processing, machine learning