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Field
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computing. With extensive experience in medical image analysis, computer vision, and AI systems through collaborations with leading institutions. Key Responsibilities: Conduct advanced research in the areas
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About us: We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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for data analysis and system control. Experience with AI or machine learning in imaging is a bonus but not required. Ph.D. in Biomedical Engineering, Optical Sciences, Bioengineering, Physics, or a related
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in image processing, quantitative analysis, and biological interpretation Proficiency in AI/machine learning tools for image segmentation, transformation, registration, or tracking Solid mathematical
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., TensorFlow, PyTorch) and transfer learning models. Proficiency in Python programming and handling medical image datasets. Strong understanding of computer vision, CNNs, and attention mechanisms. Experience
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-on experience with CRISPR. • Proficiency in electrophysiology, calcium imaging, and/or multielectrode arrays. • Familiarity with machine learning, imaging analysis, and/or bioinformatics. Online Application
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this suits a candidate with a background in optical systems / imaging, or with more experience in machine vision, or systems control and automation, or data interpretation. A candidate would not be expected
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/Data Science, Statistics or a related field. Experience with statistical analysis, including but not limited to regression analysis and machine learning. Self-motivated, with an ability to drive projects
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, including in computational tasks where data visualization, preprocessing, or interpretation can be improved. Devises and deploys custom machine learning approaches where applicable. Provides feedback and
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Details Title Postdoctoral Fellow in On-Premise Computing for Autonomous Vehicles (Computer Architecture, Machine Learning and Runtime Systems) School Harvard John A. Paulson School of Engineering