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of scientific principles. Strong molecular and cell biology techniques, as well as experience working with mouse and human models. Strong general computer skills, experience with databases and scientific
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care for patients requiring urgent or emergent intervention. The fellowship provides comprehensive training in data engineering, exploratory analysis, statistical modeling, machine learning, and artificial
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a unique opportunity to work in a cutting-edge, interdisciplinary environment, leveraging a novel in-vitro model of the human uterus and/or cutting edges machine learning techniques to make
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motivated and skilled postdoctoral researcher to lead projects related to PhacoTrainer—computer vision models for cataract surgical video recognition. Project themes will include validating PhacoTrainer
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biology, or related quantitative disciplines. Experience with longitudinal health data, machine learning, natural language processing, large language models, causal or counterfactual methods, or clinical
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demonstrated ability to design, train, and deploy large-scale models Expertise in at least one of computer vision, speech recognition, or multimodal learning, with experience in real-world technology deployment
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connections between the lab, classroom, and society. Required Qualifications: Highly motivated postdoctoral researcher with extensive experience with item response theory models, computer adaptive testing, and
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and experimentalists working across species as part of SCENE The Tolias Lab fuses large‑scale systems neuroscience with machine learning to derive principled models of cortical computation. Our newly
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Medicine are seeking to appoint a Postdoctoral Research Fellow to join a project developing and validating deep learning computer vision models to classify mosquito breeding habitat on very high-resolution
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Expertise in machine learning, including building and deploying prediction models Strong data science coding skills in programs and languages such as Python, R, Stata, and SQL Experience with research in