1,673 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Edinburgh Napier University" positions at Nature Careers
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information about the Institute see www.leibniz-fmp.de . For questions please don’t hesitate to contact Prof. Adam Lange (alange@fmp-berlin.de ) How to apply: On the FMP homepage please go to "Job-opportunities
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moss will be harvested with the purpose of sphagnum restoration at other sites or as growing media in horticultural production. The main focus of your position will be chamber measurements and data
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traceability and data quality. Collaborate with computational and clinical teams to support immune profiling projects. Assist in drafting sections of research manuscripts. Preferred skills: Hands-on experience
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confidential information. Minimum Education and/or Training: High school diploma/GED required. Bachelor's degree preferred. Minimum Experience: Minimum Requirement: 4+ years experience in administration
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communication with internal/ external members on routine matters. Create standard reports related to budget and variance, expenses etc., ensure documentation and data management while protecting confidentiality
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faculty/PIs and Administrative Staff to ensure that grant applications are submitted appropriately. This position has access to and regularly works with information of a sensitive and confidential nature
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leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine
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biopsy development. This role is suitable for candidates with either (i) a computational background (e.g. bioinformatics, data science, computational biology) who enjoy working closely with experimental
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-derived organoids and assembloids, engineered ECM environments, and in vivo mouse models, working in close partnership with the lab's computational team to generate data-rich spatial multi-omics datasets
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perturbation prediction, including the design and implementation of novel training strategies under experimental constraints, e.g., active learning and other data-efficient approachesConduct large-scale