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Field
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informatics, electrical engineering, or a related field Demonstrated expertise in machine learning, deep learning, and image-based modeling. A strong publication record in top-tier venues (e.g., MICCAI, CVPR
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learning, deep learning, imaging informatics, and large language models (LLM) is preferred. Prior working experience with popular ML packages, e.g., PyTorch, Scikit-learn, TensorFlow, Pandas, Keras, NumPy
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responsibilities Design, implement and benchmark deep machine learning models for large-scale cancer datasets that include genomics, transcriptomics, epigenomics and imaging data Collaborate closely with
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
imaging, and structured electronic health record (EHR) data. The postdoctoral researcher will work closely with the Director for Cardiovascular Artificial Intelligence Research and contribute to the design
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. We are seeking a Postdoctoral Researcher to join the team and make significant contributions to the field. The researcher is expected to have (i) strong machine learning skills to improve model
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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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. Preferred: • Strong programming skills in languages such as R/Python • Research background in biostatistics/statistical genetics/population genetics/deep learning and LLM • Experience in any of the following
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Post-Doctoral Position in Deep Learning for MRI Reconstruction at Yale University Title: Postdoctoral Associate, Yale School of Medicine Department/Division: Radiology and Biomedical Imaging
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following position Postdoctoral researcher (m/f/d) in Environmental Data Science and Machine Learning for the project BoTiKI Location: Görlitz Employment scope: full-time (40 weekly working hours) / part
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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