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Field
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intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and scientific applications. About the research project You will work in Julian Togelius' new research
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Do you want to combine high-throughput directed evolution with machine-learning analysis of deep sequencing data to engineer better antibodies? The Sormanni Lab in the Department of Chemical
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, research, and public service. Purpose: The Postdoctoral Researcher will work on research projects in the area of wireless communication system design with machine learning applications. The ideal candidate
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Missouri University of Science and Technology | Rolla, Missouri | United States | about 2 months ago
position focused on advancing crop production and physiology through field experimentation, computation (statistics and machine learning), and process-based models. The successful candidate will have an
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initiatives, and establish standards to advance machine learning ( OpenML.org ) OpenML is a popular open science platform for sharing interconnected AI artifacts (e.g., datasets, models, and benchmarks) using
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machine learning within the scope of ACAG. The postdoc will have various opportunities for professional development, including contributing to and leading grant proposal development (e.g., external
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of electrochemistry and artificial intelligence. Ideal candidates will have experience in machine learning, large language models, AI-agent development and computational workflows, with particular interest in building
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partner who focuses on the underlying machine learning models, and you will help coordinate the joint work across the two teams. The position offers a strong publication trajectory at leading HCI venues
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—integrating first-principles simulations with machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive
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of AI for next-generation microbiome research? We are looking for a highly motivated researcher to develop novel machine learning and computer vision methods for autonomous live-cell microscopy