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processes, such as rehabilitation, self-management, psychological, behavioural, supportive or lifestyle interventions, as well as complex drug regimens. Our work involves the design, conduct, analysis and
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analysis file creation, statistical analysis and/or modeling, and data visualization/reporting, including programs requiring complex combination of data from multiple sources. Participate in the reporting
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advanced statistical analysis, pathway & network analysis, Integrating complex clinical, biological and molecular data. Contributing to data visualization, figure generation and interpretation
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interdisciplinary research group focused on integrated approaches to geospatial systems analysis. We apply a complex adaptive systems approach that explicitly emphasizes behavioural models of human action-taking and
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with overall healthcare solutions. Responsible for the analysis, design, testing and implementation of the most complex health system information systems and solutions. Acts as a subject matter expert on
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. The successful candidate will use a combination of methodological approaches and techniques including realist review, co-production of complex systems map and case study analysis. They will work closely
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from preparing complex systems to statistical analysis of trajectories. 2. TASKS TO BE PERFORMED - The tasks will be carried out in the Methodological Support Unit, physically located in the building
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integration. Working at the intersection of data science and neuroepigenetics, our research aims to unravel the complex mechanisms underlying brain cell function and neurodegenerative diseases. The group is
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support.. Job role includes: Lead research into natural fibre-reinforced bio-resins and develop composite formulations Carry out physical testing, analysis, and validation of bio-composite structures
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Sophia Antipolis, Provence Alpes Cote d Azur | France | about 16 hours ago
analysis. The goal of this thesis is to advance causal AI for wireless networks by developing statistically reliable causal inference tools grounded in recent advances in simulation-aided inference and model