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initiatives in applications of genomics and proteomics Integrate sequencing data from external and public resources to improve clinical research outcome Supervise postdocs, graduate students and technical
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postdocs will be part of the Research School. The DDLS program has four strategic research areas: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and
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develop new algorithms where needed: this may include the incorporation of genomic or other omic data 2) An important second part of the post is helping to automate components of interpretation and
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maintenance of reproducible pipelines (Snakemake, Nextflow) with version control and containerization (Docker/Singularity). Multi-Modal Data Integration: Combining CRISPR or mutagenesis data with complementary
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. Preferred qualifications: Proven experience applying machine (deep) learning to imaging data. Strong programming skills, preferably in Python, and use of version control (Git/GitHub). Knowledge of open-source
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Experience with long-read sequencing technologies and related data analysis Experience with automation of lab procedures and liquid handling systems Previous project management experience Good oral and written
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databases such as KEGG, Reactome, and GO. Pipeline Development: Development and maintenance of reproducible pipelines (Snakemake, Nextflow) with version control and containerization (Docker/Singularity). FAIR
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. The analyses include e.g. data quality controls, mapping of DNA data to reference genomes, investigation of deamination patterns, analysis of sex chromosomes, analysis of mitochondrial and Y-chromosome
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version control and containerization (Docker/Singularity) Statistical Modeling: Quantitative data analysis using GLMs, Bayesian methods, or mixed-effect models to interpret complex perturbation datasets
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, including deep neural networks and relevant frameworks * Documented several years of experience in development with Python and version control systems, e.g., Git * Documented experience in large-scale data