PhD Position Algorithms for Microbial Genomics

Bacterial and viral infections pose one of the biggest threats to human health. Genomic mutations (i.e. changes to the genome sequence) can affect characteristics of the pathogen, such as virulence, drug response and vaccine escape. To monitor these mutations and to understand their impact, it is crucial to analyse genomic big data efficiently and accurately.

Genomes are studied through genome sequencing, but these technologies can only read DNA fragments of limited length. We enable biological interpretation of these sequencing data sets by developing algorithms based on graph theory, discrete optimization and machine learning. In this PhD position you will focus on strain-aware genome assembly, variant calling and strain abundance quantification for viruses, bacteria and yeasts. For example, we would like to be able to track how the prevalences of different strains in a mixed sample change over time.

**Your role:**

You will develop and implement algorithms to find, quantify and track mutations in evolving populations of viruses, bacteria and/or yeasts. Some challenges that you will be facing are:

• How to distinguish genomic mutations from sequencing errors using the latest sequencing technologies?
• How to distinguish mutations in a given gene from mutations in homologous genes?
• How to deal with large-scale structural variation?
• How to link mutations together into genomes across distances longer than the read length?

You will address these challenges by formulating appropriate graph optimization problems (e.g. minimal edge covering, minimal path covering, shortest paths, clustering) and solve these problems using techniques such as linear/integer programming or by designing suitable heuristics.

The algorithms that you develop can play an important role in pathogen surveillance, infection treatment design, and/or understanding evolution. Depending on your expertise and interests, your projects can be more applied (in collaboration with experimental groups) or more theoretical. More information about ongoing research, publications and media can be found here .

You will be part of the Delft Bioinformatics Lab in the Intelligent Systems department at TU Delft. In this project we also work together with experimental groups at TU Delft (e.g. Robert Mans, Biotechnology) as well as internationally (the Baym lab at Harvard Medical School). The Delft Bioinformatics Lab has strong algorithmic and machine learning expertise, with a profound interest in new biomolecular technologies. We teach bioinformatics courses across several programs within TUDelft and nationally, in which you may also assist.

We value different perspectives and we strongly believe that diversity drives innovation. We offer an open and safe working environment for employees and students regardless of their nationality, cultural background, gender or sexual orientation. Anyone who meets the application criteria and whose curiosity was triggered by the description above is strongly encouraged to apply.