856 image-coding-"Foundation-for-Research-and-Technology-Hellas" Fellowship positions
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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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cluster provides shared access to state-of-the-art instrumentation across multiple imaging platforms and techniques, technical expertise, and assistance in optimal image collection and analysis
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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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(this overlaps with the former themes as 2D and 3D vision is often integrated with language). Related papers by the Vision-CAIR members Theme A : Aligning Latent and Image Spaces to Connect
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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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, 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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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