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Field
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related fields. Additional optional skills and qualifications: Experience in deep learning for medical imaging. Contracting requirements: Presentation of the academic qualifications and/or diplomas
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or machine learning. Excellent programming skills in Python and deep learning frameworks A collaborative mindset and interest in socially impactful research. Experience with sign language data, multimodal
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of algorithm-hardware co-design, especially for robotics or edge AI deployment. Strong programming skills in C, C++, and Python, with experience in deep learning frameworks such as PyTorch or TensorFlow
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, Medicare and/or commercial claims data, a deep understanding of analytical methods and statistics, and advanced programming skills are therefore desired. The role will also involve preparation of graphical
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or working with complex datasets, ideally from experimental or sensor-based systems. Hands-on experience with machine learning, including deep learning, probabilistic models, or physics-informed approaches
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factors: Prior experience in developing algorithms for biomedical image processing (especially aligned with the research group's areas) and machine learning/deep learning techniques. Prior knowledge of data
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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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at the Institute and affiliated academic departments. What you’ll do: Designing, developing, and deploying modern AI/ML models—including deep learning, foundation models, multimodal architectures, and generative
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, Engineering, Physics, Medical Physics, Applied Math, Statistics, or a related field. * Strong background in deep learning (e.g., CNNs, transformers) and familiarity with emerging techniques for reasoning and
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data with good utility. This project involves development of deep learning based synthetic data generators that obtain both good utility and protection of privacy, through tailored model approximation