1,338 machine-learning "https:" "https:" "https:" "https:" "The Open University" positions at Nature Careers
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collaborators. With the help of statistics, machine learning and pathway- and network- and analyses, the goal is to improve the mechanistic understanding of disease- and treatment-associated alterations in
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or other large-scale biological data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, focusing on single-cell
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of young scientists (Master / PhD / Postdoc). Our expertise lies in quantum foundations, quantum information theory and quantum technologies. For additional information, please visit: https
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the usual documents until 1/11/2026 on the application portal of the university using this link: http://obp.uni-goettingen.de/de-de/OBF/Index/76205 . For more information get in touch with Serena Müller
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. Antonio Scialdone’s group at Helmholtz Munich, a leading European hub for AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models
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vitae, a brief cover letter outlining their research experience and interests, and contact information for three references via email to: sgong@engr.wisc.edu Research Group Website: https
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hypotheses. Develop, refine, and benchmark computational pipelines using statistical modeling, machine learning, and deep learning approaches. Conduct analytical validation studies including precision
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computational pipelines for multiplex imaging, spatial transcriptomics, single cell RNAseq, and multi-omics data integration. Lead graph-based network and machine learning analyses of tumor immune
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for experimentation, yet they remain difficult to deploy directly onboard robots due to hardware availability, latency, sampling cost, and noise. Previous work on quantum machine learning (QML) emphasize
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. Experience with applied computational methods and machine learning is preferred but not essential. Postdoctoral research experience is preferred but not essential. A thorough understanding of the fundamentals