785 image-coding-"Foundation-for-Research-and-Technology-Hellas" Fellowship positions
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online behaviour at scale, across multiple modes of communication (messages, images, video, memes and emojis) that shape participation and meaning in warfare. You will lead and manage a range of individual
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work that underpins the scientific research of the collaboration. Research Title: Thermal 3D Magnetic Imaging of Advanced Semiconductor Packages The work will entail: Our multi-disciplinary team at NIST
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, hybrid, and highly collaborative lab. This computational genomics postdoctoral fellow candidate is expected to leverage the single-cell/bulk-cell multi-omics data, spatial omics data, and image data
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. The Research Fellow will join an international team in the project “Imaging the magma storage region and hydrothermal system of an active arc volcano”, funded by the UK Natural Environment Research Council and
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Agreement number 101125383). The successful candidate will join a multi-disciplinary research team, with primary responsibilities related to imaging and examination of medieval polychromed objects in museums
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mechanisms underlying gene regulation within immunological disease models. This will include pooled and arrayed CRISPR screens in combination with the interrogation of coding and non-coding human genetic
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activities: (i) the development of novel simulation techniques, (ii) high-resolution laboratory experiments of CO2 storage, and (iii) multi-scale image analysis of laboratory data. About the work tasks The
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) high-resolution laboratory experiments of CO2 storage, and (iii) multi-scale image analysis of laboratory data. About the work tasks The appointed candidate will mainly contribute to the first main
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) with a focus on healthcare and/or biology and modalities such as computational pathology, genomics, transcriptomics or medical imaging. Strong experience with Python and at least one deep learning
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4 PhD Fellows in Deep Learning at Visual Intelligence Research Centre and UiT Machine Learning Group
next generation neural networks for advanced analysis of image and multimodal data. Central research challenges are to develop neural networks that learn more efficiently from limited data