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Details Title Postdoctoral Fellow in Modular Deep Learning School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer Science Position Description The Data
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This research opportunity aims to hire PhD students as a self-funded program. By analysing retinal images with deep learning technology, early detection of Alzheimer’s disease may be possible. By
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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of Sciences is looking for a full-time (100%) doctoral scholarship holder in the field of medical image processing and analysis using deep learning. This position is part of the ERC Consolidator project “A data
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for the diagnosis and severity monitoring of ADHD. Additionally, we would like to evaluate whether an integration of advanced signal processing, network analysis and deep learning techniques would improve
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in the field of medical image processing and analysis using deep learning. This position is part of the ERC Consolidator project “A data-driven approach to microstructure imaging” (ADAMI), funded by
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Postdoctoral Research Associate (Genomics, Data Integration, Deep Learning, AI) - Radiation Oncology
Job Description Position Summary We invite talented post-doctoral candidates with an interest in cancer genomics, multi-omic data analysis, algorithm design, and/or deep learning to apply for a
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The University of Virginia seeks applicants for a Deep Learning Research Scientist to join the Research Computing team. Deep Learning, and AI in general, are used in research for multiple
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Intelligence and Deep Learning at NYU Shanghai. This position requires an experienced professional with a deep understanding of AI research, interdisciplinary collaboration, and administrative leadership
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SELF-FUNDED 3.5-YEAR PHD – Automatic segmentation of femur using deep learning combined with phantomless calibration for rapid personalised fracture risk predictions in clinical applications