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dissolution behaviour Analyse time-resolved experimental data using statistical and machine-learning approaches, and validate predictive models using independent experiments Design and conduct controlled
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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deficiency but potentially also PSC and MASLD), exploring risk factors and prognosis of diseases where histopathology is essential. The position requires a high level of statistical/mathematical competence
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candidates whose expertise falls within one or more of the following areas: computational and mathematical modeling, statistical modeling, machine learning, network science, bioinformatics, applied mathematics
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statistical analyses will be performed primarily using R. The research is carried out in a multidisciplinary environment and in collaboration with clinical researchers and other relevant partners. Work duties
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. Plant science/ecology, especially related to forest ecosystems. Computer programming. Data analysis (machine learning, statistics, numerical analysis, time-series analysis, etc.). Quantitative methods in
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skills in experimental design, laboratory and field research on animals, statistical modelling and programming, evidence synthesis, climate modelling, open and reproducible research, scientific writing
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mechanical modelling, such as finite element analysis (FEA). experience of working with healthcare professionals and patients. strong knowledge of statistical methods, including non-parametric methods
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communication and presentation A competence to think logically in biological terms, as demonstrated in research publications Expertise in R and foundations of statistics To be eligible for employment as a
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with fungal community, genomic or transcriptomic analyses Knowledge of multivariate analyses and statistical modelling Excellent communication skills in written and spoken English We consider it a merit