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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 21 hours ago
Job Requirements REQUIRED: PhD in a field of quantitative science; strong background in machine learning and signal processing; experience with and commitment to rigorous and reproducible
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. The ideal candidate will possess not only a deep conceptual understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. We
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join a highly collaborative, interdisciplinary research environment focused on developing and applying cutting-edge machine learning and deep learning methods to abdominal imaging. Working alongside
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used by a PhD student, likely starting October 2027, to initiate development of a deep-learning architecture. The refined batch of synthetic data will be used by the PhD student to finalise the deep
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 21 hours ago
cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and demonstrated ability to effectively
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dedicated researcher with a PhD in mathematics, electrical or mechanical engineering, computer science or a related field, and a strong methodological background in machine learning. The ideal candidate has
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learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop and train deep learning models (CNNs, ...) data to predict IPLSs
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qualifications include: Strong research experience in deep learning and foundation models, including experience with pre-trained models, fine-tuning, transfer learning, or self-supervised learning. Experience with
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
that could eventually be used in the clinic. The Postdoctoral Research Associate will develop diagnostic and prognostic deep learning models for Alzheimer’s disease, using rich existing multimodal datasets
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multilingual source material. You will have a strong understanding of deep learning and natural language processing, with practical experience of training, adapting and deploying large language models. You will