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Postdoc in Genetic Epidemiology – Statistical Genetics | Human Technopole, Milan Build the science that shapes the future of human health. Application closing date: 26.02.2026 Join a place where
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Postdoc position in method development in human statistical genetics, with a focus on classificat...
University. Dr Speed's research involves developing statistical methods for better analysing data from genome-wide association studies (GWAS), with a particular focus on improving our understanding of human
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data) datasets in cohorts of various ethnicities. The Di Angelantonio-Ieva group is seeking to recruit a highly motivated Postdoc in Genetic Epidemiology/Statistical Genetics. The postholder will be
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evaluation of predictive models and statistical approaches to understand treatment outcomes. Integration of diverse data types to identify features associated with therapeutic response, resistance, or toxicity
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scientific field (e.g. computer science, data science, mathematics, statistics, engineering, physics, or related). Provable deep learning track record and practical expertise (e.g. with VAEs, GANs, diffusion
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oral microbial communities mediate differences in oral and systemic health using an evolutionary health framework. There will be a specific emphasis on applying advanced bioinformatics and statistical
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scientific conferences/journals. Qualifications: Ph.D. in genomics/computational biology/bioinformatics/statistics/computer science or related discipline Experiences in Unix/Linux shell Proficient in at least
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and spatial omics analyses performing quality control, preprocessing, integration, annotation, visualization, and downstream statistical analyses contributing to data management, workflow development
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-based screens, single-cell or spatial transcriptomics). Experience with reproducible research environments (e.g., Docker, Snakemake, Nextflow). Strong mathematical / statistics background At HT, your
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Postdoctoral Research Associate - Hybrid Computational-Experimental Scientist in Bacterial Drug Resp
to antibiotics and host-like conditions. • Develop and apply statistical or machine-learning methods for interpreting single-cell and genomic datasets. • Work closely with wet-lab scientists to design perturbation