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to the development of non-destructive methods to monitor natural degradation, using advanced analytical and statistical approaches. • Develop, implement, and refine models that integrate micro-environmental
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programming and quantitative analysis (e.g., statistics, scientific programming, numerical simulation) • Excellent written and spoken English (working language is English). • Ability to work independently
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as evidence of ongoing CIN. This PhD will work at the leading edge of single-cell transcriptomics and modern statistical/AI methodology to address this gap using in-house developed cell atlases. We
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-cell transcriptomics and modern statistical/AI methodology to address this gap using in-house developed cell atlases. We will develop and benchmark approaches that infer copy-number changes and CIN
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experience (e.g. student assistant post) • Preferably demonstrable experience in academic writing for publication (e.g. first or co-authored peer reviewed papers) • Well-developed statistical software skills
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-authored peer reviewed papers) • Well-developed statistical software skills (preferably in R, Python, GIS) • Competences in quantitative research methods – ideally knowledge of several of the following
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well as fungal transformation is appreciated Experience with fluorescence microscopy Knowledge of programming, R, statistical analysis or bioinformatic is strongly appreciated Excellent English proficiency in
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, Medical Statistics and Informatics (EPICENTER), Medical University of Innsbruck Supervisor: Patrick Rockenschaub Start: as soon as possible (flexible) Duration: 3–4 years, fully funded In this ERC-funded
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-MEDIC (ERC Starting Grant) Host: Institute of Clinical Epidemiology, Public Health, Health Economics, Medical Statistics and Informatics (EPICENTER), Medical University of Innsbruck Supervisor: Patrick
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) • Preferably demonstrable experience in academic writing for publication (e.g. first or co-authored peer reviewed papers) • Well-developed statistical software skills (preferably in R, Python, GIS