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methods for causal inference in observational data, is strongly preferred. Using various existing large datasets with rich information for knowledge synthetisation and triangulation over the course of the
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ideal candidate will have an interest in ecological modelling, spatial analysis, and conservation planning, with experience in ecological fieldwork or quantitative data analysis being advantageous. Full
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communication skills in English Additional preferred expertise and experience Experience with longitudinal data analysis and advanced statistical methods (e.g. linear and generalized mixed-effects models, growth
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plans, analysis, and presentation of data as well as other assignments related to scientific development (e.g. writing manuscripts, reviews, and supervising/teaching other fellows). Studies focus
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reuse of energy systems analysis processes. In line with FAIR and Linked Open Data principles, you will design interfaces that enable the smooth processing of big data in the context of scientific, AI
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Do you want to work with healthcare data and develop your data science skills? Do you want to make a difference in healthcare by understanding the reasons why an implant fails or succeeds? Around 1
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, immunology, high dimensional omics analysis, or mass spectrometry. • Strong data analysis skills using R, Python, or equivalent. • Excellent verbal and written communication skills. • Ability to work both
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such as scRNA-seq, bulk RNA-seq, DNAm analysis, proteomics and clinical data integration to determine how IFN-I responses change during ageing. Application Procedure Applicants should submit a cover letter
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laboratory techniques and data analysis (ICP-MS, LC-MS or GC-MS) Preferably experience in scientific publication Skills and interest in teamwork and interdisciplinary collaboration Skills and interest in
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such as landslide movement style, runout, and how landslide hazards evolve over time. This Ph.D. project will leverage the analysis of new time-series data from cloud-based satellite image archives