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, Social Sciences , Biomedical Informatics , Causal Inference , Computational Social Science , Data Science and Information , Data Visualization , Deep Learning , High dimensional Data , Large Language
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Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated working experience
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Department of Computer Science and the Data Science Institute. Learn more about the lab and its research here: https://nunez-comp-mental-health-cancer-care.github.io/ . RESPONSIBILITIES Reporting to Dr. John
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed
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Biology, Bioinformatics, Statistics, or a closely related discipline, and have an strong record of research productivity. The ideal candidate will have experience in deep learning, generative models
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, and the ability to read related scientific papers on cancer combination therapy. It would also require expertise in relevant AI methodology, such as deep learning architectures for property prediction
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refractive-index imaging of complex samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue
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We are seeking creative and energetic candidates with strong experience in multimodal machine learning and human behavior analysis and modeling for a one-year Postdoctoral position. Using recent
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an excellent environment for deep innovation, out-of-the-box thinking, and creative problem solving. We will teach you what you do not yet know through mentoring, peer support, and many educational opportunities
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. Research will focus on neural data integration, neural circuit modeling, biologically grounded representation learning, and foundation models for neurobiology. The Postdoctoral Fellow will work closely with