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. Experience in the implementation of mathematical or statistical models and model fitting, including Bayesian model fitting, is desirable but not essential. Familiarity or experience of management and analysis
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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
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Strategic Interest We welcome applications from all disciplines aligned with one of the strategic research priorities below: - Clean Technologies: Net zero innovation, sustainable energy, circular economy
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societal impact. An independent, international selection panel will select 20 fellows for 36-month positions through an open, merit-based process. We offer : A full-time position for 36 months An average net
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and immunogenomics, and cancer-specific bioinformatics. Develop skills in study design, data interpretation, presentation, and manuscript writing. Develop skills in networking, career planning, teaching
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, expand your network, and shape the future of AI-enabled scientific discovery. Why Apply NTU is a world-leading research-intensive university consistently ranked among the best globally. In the 2025-26 U.S
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. Situated in the heart of Singapore’s Singhealth Academic Medical Center, Duke-NUS offers postdocs access to advanced core facilities, integration with national clinical networks, and a uniquely collaborative
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research initiatives such as the Met Office Academic Partnership and Birmingham’s Turing University Network . The recently renovated Elm House, is a building fully dedicated to open, inclusive and inspiring
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of seed gene networks”. This project aims to use reverse genetics, cross-species complementation and single cell next-generation sequencing approaches to investigate how the gene networks that regulate
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where