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statistical analysis. Supervise and mentor junior researchers, PhD candidates, and technical staff within the team. Manage the reporting process and scientific production of the research activities, such as
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Preference will be given to those with proven knowledge and experience in undertaking research related to social network analysis. For the project information and job inquiries, please contact the Principal
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-ECOAQUA research institute. Main Responsibilities The researcher will perform the following tasks: Marine sampling of rhodolith algae using dredges from the surface across the project's network of Eastern
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on the track of Cyber-Human Physical Systems seeks researchers to advance Systems Science and Engineering by modeling human behavior in complex dynamic networks addressing global challenges. Emphasizing Cyber
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in electrophysiological methods. We collaborate with a network of local mental health centers for recruitment of patient populations. The fellow will be involved in all aspects of research including
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an interdisciplinary environment; Willingness to travel within Europe for network activities and secondments (including research stays at i3S, Portugal). Desirable Experience in glycan analysis or glycomics workflows
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, Air quality, PFAS, Particles, Non-target analysis, Data analysis. Location of the PhD project: UPVD (Université Perpignan Via Domitia), Perpignan, France: 1st year of the PhD INERIS, Verneuil-en-Halatte
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required to carry out research activities in various areas. On the theoretical side, they will contribute to the development of methodologies for the analysis of complex networks with applications
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interpretation of experimental data; Teamworking experience; Publication record commensurate with career stage. Demonstrate proficiency with data analysis tools (e.g., Python/Matlab/Origin). Have the ability
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Mathematics at Grade C or above or NVQ level 2 Administration or equivalent. Experience in a comparable role to include: Demonstrable experience in MySQL, php and html programming; Experience of data analysis