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at the University of Utah invites applications for one a 1-year post-doctoral researcher for interdisciplinary work spanning machine learning, applied mathematics, computer science, and material sciences, with a
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postdoctoral researcher with a focus on AI trustworthiness modeling on multimodal data and machine learning models. The Department of Computing Science has been growing rapidly in recent years, with a focus on
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to cover a wider variety of physics use cases. Developing methods to make machine-learning-based models portable and interoperable. Leading the definition of containerized and networked “Models as a Service
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or recent Ph.D. in applied/computational mathematics, computational science, chemical engineering, or related disciplines. Ideal candidates will have strong past experience in scientific machine learning
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work experience as a post-doc and/or in an R&D position, excluding time associated with family planning, military service, illness or other life-changing events. Brookhaven National Laboratory is
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position, available until December 2027. Flexible work arrangements can be negotiated with the right candidate. Be part of the Australian Institute for Machine Learning – the largest computer vision and
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classification algorithms including machine learning); and the output data and interpretability. The project “SORS in the community” is funded by the EPSRC (https://www.ukri.org/news/new-tools-aim-to-improve-early
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Overview We are seeking a highly motivated postdoctoral researcher with expertise in artificial intelligence and machine learning (AI/ML) to join our interdisciplinary team at Duke University. The postdoc
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Institute for Machine Learning – the largest computer vision and machine learning research group in Australia – and contribute to world-leading research projects at the Centre for Augmented Reasoning
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, biophysics, or in a related interdisciplinary field. Experience with AI and machine learning tools is preferred. Applicant instructions: Applicants should be prepared to upload the following documents when