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Field
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encouraged to apply: Biologists with mathematical, computational, or programming experience. Prior experience with mouse models, motivated to learn new techniques including advanced optical imaging
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. Qualifications: Doctorate degree required Background in optical imaging, data and signal processing Expertise in machine learning Experience in human subject measurements Background in optical tomography as
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. Sensing, Machine vision, image processing and data fusion. Inline manufacturing process monitoring and control. Major Duties/Responsibilities: Develop foundation AI models under various projects. Develop
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work closely with CFN Electron Microscopy group members and computer scientists at Brookhaven. You will be professionally mentored by Dr. Judith Yang and Dr. Sooyeon Hwang and receive guidance from Prof
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Job Description This position offers a unique opportunity to work on advanced projects at the intersection of AI, machine learning, computer vision, and multimodal learning, focusing on advancing
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unified framework that can understand and generate human-like text while simultaneously processing and analyzing medical images. The model will be trained on a diverse dataset of medical images and
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using machine learning and deep learning techniques to generate indicators that allow remote monitoring of restoration. Knowledge of remote sensing (e.g. GEDI, LiDAR, multispectral) and programming (e.g
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Electrophysiological signal processing of, e.g., EEG, ECG, EMG, etc. Health data science, incl. modern machine, and deep learning methods, Cloud-based platforms like MS Azure or Google Colab Health data standards, like
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Postdoctoral Associate to advance cutting-edge research in machine learning (ML). Our lab explores the intersection of artificial intelligence, and human-computer interaction, striving to create technologies
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Computer Science and Electrical and Computer Engineering departments, and the School of Medicine. The group emphasizes collaborative and multidisciplinary work and brings together expertise from machine learning