114 machine-learning "https:" "https:" "https:" "The University of Edinburgh" Postdoctoral positions at Cornell University
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Postdoctoral Associate: Neurodegeneration – Hu Lab, Weill Institute (Research & Innovation) Postdoc Associate: Neurodegeneration – Hu Lab, Weill Institute Weill Institute The Fenghua Hu lab (https
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sciences, computer science, machine learning, and education research. Research Themes The research themes identified for the NTO postdoc include, but are not limited to, the following: Developing
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and research or policy positions in government. If you any questions about the position, please email Emma Spence (es957@cornell.edu ) for more details. To apply: Visit https://academicjobsonline.org
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in government. If you any questions about the position, please email Emma Spence (es957@cornell.edu ) for more details. To apply: Visit https://academicjobsonline.org/ajo/jobs/31900 and submit a
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crane. The successful candidate will build reproducible machine learning pipelines, integrate detections into spatial ecological models, and generate conservation-relevant outputs for regional partners
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Responsibilities will vary depending on the Fellow’s background, but may include: Developing machine learning, optimization, or simulation models to improve clinical operations and resource allocation Advancing
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opportunity to align with the most relevant academic department in the College of Architecture, Art, and Planning and teach one course per year subject to department needs. The Postdoctoral Associate will be a
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partners in the digital health and health delivery ecosystem. Research Responsibilities Responsibilities will vary depending on the Fellow’s background, but may include: • Developing machine learning
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To apply: https://academicjobsonline.org/ajo/jobs/31821 Applicants should submit: CV Cover Letter 1–2 page research statement describing research interests and how they relate to the Cornell Global AI
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Postdoctoral Associate as part of Cornell’s Active Learning Initiative for the AYs 2026 – 2028. We invite applications from candidates with a specialization in any area of History of Art broadly defined, and