Job offer details

Postdoctoral researcher to work on methods-development for adaptive long-read sequencing

 Postdoctoral researcher to work on methods-development for adaptive long-read sequencing

The Biomedical Informatics Lab at ETH Zurich led by Prof. Gunnar Rätsch is seeking a highly motivated bioinformatics postdoc with a strong background in algorithm development, data structures, experience in programming and supervising students.

You will join a team of senior researchers and graduate students to work at the interface of computer science theory and biomedical application in a highly inspiring environment. The project is set up as a close collaboration between members of the Department of Computer Science at ETH Zurich and the Medical Virology Lab at the University of Zurich and provides opportunities for both theoretical work as well as practical engineering.

Job description

In this role, you will be responsible for developing and implementing innovative algorithms and data structures to improve the use of long-read sequencing technologies for adaptive sequence sampling. Specifically, this concerns the development of algorithms to decide in real-time whether a DNA/RNA fragment being currently sequenced should be further considered or discarded. Ultimately, the project aims for solutions that are feasible to run on constrained hardware resources. You are expected to present your findings at conferences in the field and to draft manuscripts aimed for peer-reviewed publication. If you are passionate about bioinformatics, have a strong background in algorithm development and data structures, have programming expertise, and experience supervising students, we would love to hear from you!

Your profile

You have: 

  • a Ph.D. in bioinformatics, computer science, or a related field,
  • experience in developing and implementing algorithms and data structures for biological data analysis,
  • solid experience in a high-level programming language (C/C++, Rust, Python, or similar),
  • a track record of leading research projects to successful completion,
  • experience in code optimization and benchmarking,
  • the qualification to lead a team implementing software solutions to complex problems,
  • the ability to work in an interdisciplinary setting and to work across domain boundaries,
  • excellent communication and interpersonal skills. 
  • In addition, familiarity with long-read sequencing technologies and the challenges of analysing large-scale genomic data is desirable.
  • Experience in sequence assembly is highly appreciated.

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