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interested in using AI to unravel the mysteries of the brain? Do you want to perform cutting-edge NeuroAI research and leverage deep learning to understand human vision? Then check out the vacancy below and
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in developing bioinformatics pipelines (Python/R/Bash) for genomic (NGS) and medical imaging data analysis, implementing artificial intelligence and deep learning techniques. - Experience in developing
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As a PhD candidate, you will: - Develop and train deep-learning
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of deep neural networks in the field of adaptive processing of graph data (Deep Graph Learning) . The developed novel approaches will be applied to case studies in bioinformatics Requirements Additional
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Science, Computer Engineering, Electronic Engineering (or related disciplines). A strong record of research quality, commensurate with career stage in AI, including but not limited to: machine learning, deep learning
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 6 hours ago
expose the successful candidate to cutting-edge genome editor engineering approaches and the delivery of these reagents in vivo via AAV or lipid nanoparticles. The successful candidate will also learn
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within the centre. You will be part of a network of young researchers in deep learning in the Visual Intelligence Graduate School https://www.visualintelligence.no/about/vigs Jarli & Jordan/ UiO via
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evaluate deep learning models for MRD detection and characterization Collaborate with multidisciplinary teams across Dana-Farber Cancer Institute, the Broad Institute, and more Mentor and guide junior staff
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closely related quantitative discipline. Demonstrated experience with large-scale deep learning models and modern ML frameworks (e.g., PyTorch, JAX, Transformers), including training, fine-tuning
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and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures