2,340 machine-learning "https:" "https:" "https:" "UCL" "UCL" Postdoctoral research jobs in United States
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for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins. There will be potential
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these questions, we use a novel multi-omics approach that integrates high-throughput imaging and machine learning methods with CRISPR/Cas9 screens and saturation mutagenesis to answer central questions about the
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: Education: Ph.D. in machine learning, computer science, engineering, physical science or related technical discipline. Experience: Expertise in developing and training AI models Proficiency in Python
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worldwide and BNL is responsible for data compilation in North America. In order to make full use of the data in EXFOR, experience in data science, machine learning (ML) and applications of artificial
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, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual
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, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual
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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
: 265566 Vacancy ID: PDS003971 Position Summary/Description: The Miao Lab in the Computational Medicine Program and Department of Pharmacology at the University of North Carolina – Chapel Hill (https
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diseases, Genome Biology, 2024 S. Hudaiberdiev et al., Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits, PNAS, 2023 S. Li et
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, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race