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Signal Processing and Image Analysis group (DSB), Section for Machine Learning, at IFI. DSB has seven full-time and five adjunct positions and carries out research across image analysis and machine
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Processing and Image Analysis Group, Section for Machine Learning, Department of Informatics. You will be part of Visual Intelligence and the DSB group. For more information about the position see https
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methodologies Experience with machine learning techniques Experience with pipeline development and testing (gitlab, simulated light curves…) Ability to work independently and to collaborate in an international
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the Digital Signal Processing and Image Analysis Group, Section for Machine Learning, Department of Informatics. You will be part of Visual Intelligence and the DSB group. For more information about the
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for evaluation by the closing date. Only applicants with an approved doctoral thesis and public defence are eligible for appointment Strong programming and artificial intelligence/machine learning skills Interest
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Qualifications ? Ph.D. in Physics, Materials Science, or a related field with a concentration in electron microscopy methods ? Experience in the collection and processing of TEM/STEM data ? Computer programming
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 16 hours ago
experience and interest in mental health, suicide prevention, health professions training, and/or military health, as demonstrated through any or all of the following: A record of scholarly publication and
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. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning models that forecast
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in Spatial Omics and Multi-Modal Data Integration Duties & Responsibilities: Develop computational and machine learning methods for spatial omics data (spatial transcriptomics, spatial proteomics