PhD student in plant science with focus on deep-learning conifer genomics

Project description

Ever advancing high-throughput DNA sequencing technologies continue to produce genome assemblies of larger genome sizes, such as conifers (10-40 Gb, 3-15x of human genome), wheat (15 Gb, 5x), axolotl salamander (32 Gb, 11x), and giant lungfish (43 Gb, 14x). Genome annotation (identifying various functional sequence elements) presents one big challenge in analyzing large genomes, for example, to identify transposable elements (TEs), major component of most plant genomes especially those large genomes. Deep-learning models (currently used for computer vision and natural language processing), which can encode high-dimensional genome sequences into vectors and learn and resolve the sequence complexity, offer promising solutions for efficient identifying sequence elements (such as TEs) from genome sequences.

The PhD project focuses on plant genomics and the development of deep-learning driven computational tool. (1) This project aims to develop new deep-learning driven TEs identification tools to further decrease the computational demand for large genome analysis. (2) To develop such an efficient computational tool, this project will first construct a comprehensive TE dataset to capture the sequence diversity of TEs by collecting genome data from hundreds of plant species. (3) This project will also look into TEs integration profile by predicting TE insertion from genome sequences. This project will potentially develop a set of new analytical tools and reveal a more insightful picture of TE movement and evolution. 

This PhD position is focused on developing and applying deep-learning driven methods to advance our understanding of complexities of TE in large plant genome. The successful candidate will work at the interface of machine-learning, computational biology, and molecular biology and will have the opportunity to collaborate with experimentalists and computational scientists across a wide range of disciplines.

Admission requirements

To fulfil the general entry requirements for studies at third-cycle level the applicant must have qualifications equivalent to a completed degree at second-cycle level or completed course requirements of at least 240 ECTS credits including at least 60 ECTS credits at second-cycle level. To fulfil the specific entry requirements to be admitted for studies at third-cycle level in Plant Science at Umeå Plant Science Centre, the successful candidate must have completed 90 ECTS relevant to the doctoral thesis project. Out of this, at least 30 ECTS have to be in a subject closely related to the research topic of the graduate program. Applicants who have acquired equivalent skills in some other educational system in Sweden or abroad are also eligible.

For this position, we are looking for a person interested in plant biology and machine-learning. You should have an academic background in plant biology, machine-learning, bioinformatics or a related field. The following achievements, skills and/or knowledge are also required:

The PhD student is expected to play an active role in developing this doctoral project and in the department. In addition, the PhD student is expected to have a scientific, structured, flexible and result-oriented approach to their work. The assessments of the applicants are based on their qualifications and presumed ability to take part in doctoral education.

The application