Bioinformatics applied to pangenome graphs

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  • University/Institute Name University/Institute Name
  • University/Institute Name Department of Health & Human Services
  • Position Funding Type Fully funded (e.g. RA, TA, Scholarship, etc.)
  • Position Duration3 years
  • Attendance Type On Campus
  • Application deadlineExpired

Position Details (PhD)

In this position, you will develop algorithms and computational methods to deal with the analysis of large datasets from modern and ancient sources. More specifically, these algorithms will be aimed at developing novel methods using pangenomes graphs.

Traditionally, sequencing projects used a single linear reference genome to conduct analyses. Recently, groups have proposed replacing this single reference with a graph encompassing all known diversity within a species. While this approach has yielded exciting results, a lot of work remains to fully exploit the power of pangenomic graphs. The Modern and Ancient Genomes group at DTU are pioneers in terms of developing probabilistic inference methods on pangenome graphs. The position involves computational methods development which means that a large portion of time will be dedicated to algorithm development and coding, likely in C++.

You must have a two-year master’s degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master’s degree and a bacherlor’s degree. Ideally, your degree should be in computer science, mathematics or biological science with a focus on quantitative and mathematical aspects.

More specifically you should ideally have the following qualifications:

  • Knowledge of a programming language like Python, Perl, C++ and/or Java
  • Ability to work in a UNIX environment, ideally in a high-performance computing environment
  • Thorough understanding of basic algorithms and data structures used in computer science
  • Knowledge of probabilities and statistics
  • Firm grasp of first-year university mathematics (differential calculus/linear algebra)
  • Experience in bioinformatics and knowledge of metagenomics are a plus
  • Expertise in next-generation sequencing data generation and processing are also a plus but not required
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