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that contribute to new breeding tools and accelerates discovery. The position will offer opportunities to explore several approaches to solve the optimisation strategies including simulation-based, big data
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
diversity and brings together expertise in software engineering, big data, clinical informatics, and medicine. Key Responsibilities Collaborate with researchers to design, develop, and refine large language
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investigating adaptive neural circuits underlying distinct forms of behavioural flexibility. The postholder will be responsible for designing, developing, and operating advanced large-field or multi-brain region
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multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a team with a strong track record in Big Data analytics in child mental health, helping accelerate the move toward
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instruments Analysing and interpreting data Writing experiment proposals, presenting results at conferences and publishing in peer-reviewed journals. Work in laboratories and large-scale facilities. Travel
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Accountabilities PhD in biochemistry, Biomedical Sciences or Chemistry. Mass spectrometry-based proteomics. Data analysis of large proteomics datasets. Experience in cell culture and molecular biology. Languages
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, and contributing to a robust data infrastructure that makes large-scale, multimodal datasets FAIR and AI-ready. You will publish findings in high-impact journals, present at major international
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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in core methods of machine learning/artificial intelligence. ● Experience with data warehousing and building large, curated datasets with protected health information, suitable for training large