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with a main emphasis on genome assembly. The research group is part of the Algorithmic Bioinformatics lab at University of Helsinki. The amount of sequencing data has increased enormously during the last
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experimentalists and clinical partners to understand and model biological processes and disease phenotypes Supervise and mentor students, and assist other team members in experiment planning, data processing and
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graduation) in bioinformatics, biochemistry, biotechnology, or related field Experience in mass spectrometry-based data analysis Solid background in statistics and omics data processing Proficiency in R
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is decoded through transcription, and how the defects in this process can be used as a basis for therapy. Currently, we are probing how the crosstalk between transcriptional kinases and other factors
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to the development of reproducible analysis workflows and pipelines. You have a PhD in bioinformatics, computational biology, data science, computer science, genetics or other relevant field. You have successfully
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, including excellent skills in programming and high-performance computing Research experience in a relevant field, e.g. functional genomics, transcriptomics, bioinformatics, bioimage analysis, and/or single
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relevant programming languages (Python, Perl, R). A good understanding of molecular biology, genomics experiments and bioinformatics is expected. Candidates with proven track record in terms of publications
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models, algorithms and data structures for high throughput sequencing data with a main emphasis on genome assembly. The research group is part of the Algorithmic Bioinformatics lab at University
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-application-process-in-a-nutshell For the postdoctoral researcher’s position, the appointee shall hold a doctoral degree and have the ability to conduct independent scholarly work as well as the teaching skills
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intersection of computational biology, biotechnology, and precision medicine. You will contribute to the development of innovative bioinformatics tools and perform in-depth analysis of cutting-edge datasets