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facilities for data acquisition v) Contribute to the analysis of structural data vi) Contribute to the dissemination of results (rapid publication expected) The project will be carried out at IGDR (Rennes
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contribute to the preparation of reports, manuscripts, and other written outputs. Requirements for both posts are: PhD in Nutrition, Epidemiology, Statistics. Expertise in data analysis: nutritional biomarkers
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, transcriptomics, and proteomics. - Perform computational analysis of high-dimensional datasets, integrating multi-omic data where appropriate. - Interpret findings in a broader biological and
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brain development and in combining experimental and computational approaches to study developmental mechanisms. Experience in either advanced stem cell-based modeling or transcriptomic data analysis is
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will maintain accurate and detailed records of procedures performed and outcomes. They will assist in management analysis and presentation of laboratory data and clinical data. Using this data
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Postdoc in experimental studies of phase behavior of ABC-miktoarm star block copolymers in thin f...
the development of GISAXS data analysis methods and is responsible for analyzing the data obtained. The postdoc will also prepare and characterize the thin films studied using e.g. spin coating and AFM. The optimal
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or Functional ultrasound imaging or Electrophysiology (neuropixels) in behaving animals Quantitative data analysis and computational modeling of network activity Data acquisition systems, signal processing and
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-throughput lipid residue analysis, involving GCMS and GC-c-IRMS. They will work on the interpretation of the data and integration with other sources of evidence provided by the rest of the project team. Skills
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Requisition Details GENERAL REQUISITION INFORMATION EEO Statement: UNMC is an Equal Employment Opportunity Employer, including an equal opportunity employer of protected veterans and individuals
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data