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Additional preferred expertise and experience Experience with longitudinal data analysis and advanced statistical methods (e.g. linear and generalized mixed-effects models, growth curve analysis and structural
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species and vegetation ecology), advanced statistical modelling using R software, and conducting fieldwork under harsh environmental conditions. A successful applicant should have good skills in English and
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statistical methods (e.g. linear and generalized mixed-effects models, growth curve analysis and structural equation modeling). Familiarity with the opioid and endocannabinoid system and topics related
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practical organization of study activities Data analysis using advanced statistical and epidemiological methods Writing and publishing scientific articles in peer-reviewed journals Presenting findings
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expertise, this may involve: Neurological data collection Neuropsychological assessment of young children (<3 years) Coordination and practical organization of study activities Data analysis using advanced
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candidate will drive the full experimental pipeline: computationally guided construct design; expression and purification of proteins; structural determination to resolve active-site architecture; activity
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include, but are not limited to, in vitro culture of primary human cells and cell lines, organ-on-chip models, molecular biology, diverse immunological assays, advanced flow cytometry, fluorescence
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and floral resource availability on pollination services and apple yield”. This research aims to explore how the habitat surrounding orchards, orchard structure, and manipulation of floral resources can
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structured manner. Applicants must have good collaboration skills and the ability, willingness, and commitment to work in a multidisciplinary team. Admission to the PhD programme at the Faculty of Law requires
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; expression and purification of proteins; structural determination to resolve active-site architecture; activity assays to quantify kinetics and selectivity under operationally relevant conditions; and multi