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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 2 hours ago
, automation, and data analysis Experience running PyFR on HPC systems Ability to work both independently and collaboratively within a research team Preferred Qualifications: Experience working in Mars
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analysis of complex biomedical datasets. The research environment is highly interdisciplinary, bringing together expertise from machine learning, statistics, data science, and healthcare research
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guidelines, and policy recommendations; Supervise and guide Research Assistant(s) in data collection, literature review, and preliminary analysis; Coordinate with project partners, stakeholders, and the
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of developing ethics plans and obtaining ethics approval; data collection/analysis; experience of working in a multi-disciplinary environment and coordinating work across research partnerships; an evidenced
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Research Assistant/Assistant Professor (Postdoctoral Researcher) for the project 2025/57/B/NZ8/03444
sciencesEducation LevelPhD or equivalent Skills/Qualifications Knowledge and experience in the following areas: processing, analysis and interpretation of remote sensing data, in particular airborne laser scanning
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related discipline. A PhD in one of these fields and proven experience in programming for data analysis are required. Previous experience in neurophysiological data analysis (scalp EEG, intracranial
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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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-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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blood samples to advance patient care. This role will involve developing computational models (statistical, machine learning, etc.), and using them to perform high throughput analysis of clinical data
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research. Responsibilities : Develop and apply machine learning and data-driven methods for the analysis and interpretation of synchrotron X-ray scattering data, including diffraction and total scattering