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Field
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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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new MRI techniques for motion-robust imaging, real-time image processing, and/or deep learning. The work includes MRI pulse sequence design for various MRI techniques and technologies, algorithm and
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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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journal publications dependent on your background discipline(s) and should hold sufficient theoretical knowledge of deep learning-based methodologies as well as working with real-world data. Informal
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learning for dynamical systems, system identification and control systems. This can involve co-tutoring of PhD students or early R&D cooperative studies. contributing to the development of advanced benchmark
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. Qualifications • PhD degree in computational biology, bioinformatics, mathematics, physics, engineering, computer science, or a related field. • Computational background in areas such as AI/deep learning
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learning, deep learning, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific
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of the position, the applicant chosen is expected to be physically present on a regular basis and actively participate in the research activities of the Department. Qualifications The applicant must hold a PhD or
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. Machine learning/atmospheric science/satellite data processing experience preferred but not required Creative problem-solving skills and ability to work independently *Candidates with a PhD in other
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it is not required that the candidate possess a deep theoretical understanding of AI, practical proficiency in using AI will be considered in the selection process. Applicants must hold a PhD prior to