Postdoctoral Fellow
vor 1 Woche
Your role
would be to analyse large-scale microbiomics data (metagenomes, metatranscriptomes, metabolomes etc.) for biological discoveries by data integration. The primary focus will be the human gut microbiome, with data coming from various internal and external projects, often involving hundreds of individuals. For example, multiple cohorts of patients with different stages of liver disease will be analysed, partially with multiple time points, deep omics characterisation, medical drug treatment and clinical follow-up data. Available projects will be, for example, (i) dissection of the impact of therapeutics, environmental factors, and the gut microbiome on human health, (ii) integration of multi-omics data for functional interpretations, and (iii) analysis of the diversity of the microbiome across the globe both in terms of structure (species and strains) and function (e.g. detection of novel functional modules), which may also include analyising environmental or animal microbiome data. The work aims at biological discoveries and will comprise both methodological and applied aspects.Apply nowClosing date: 28 July 2024
Contract duration: 2 years
Grading: Postdoctoral Stipend
Reference number: HD02620
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You have
A PhD degree in Biology, Bioinformatics, Computer Science, or a related field Proven biological background knowledge and bioinformatics skills related to the group’s research, especially in working with metagenomic sequencing data Experience in data integration, statistical analysis and visualisation, e.g. in Python or R Ability to work both independently and in a team Willingness to learn new skills as projects require Good communication, interpersonal and English language skills
You might also have
Experience in working with the Linux command line and compute clusters Experimental expertise in microbiology Familiarity with processing next-generation sequencing data and performing sequence analysis (such as multiple sequence alignment, phylogenetic analysis, or Hidden Markov Models) Competence in processing large microbiomics datasets Experience in machine-learning methods-
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