PhD Student in Plant Science with Focus on Deep-Learning Conifer Genomics
5 days ago
The project focuses on plant genomics and the development of deep-learning driven computational tools. The goal is to develop new deep-learning driven transposable elements (TEs) identification tools to further decrease the computational demand for large genome analysis.
Key Objectives- Develop a comprehensive TE dataset to capture the sequence diversity of TEs by collecting genome data from hundreds of plant species.
- Predict TE insertion from genome sequences to gain insights into TEs integration profile.
- Develop a set of new analytical tools to reveal a more insightful picture of TE movement and evolution.
The successful candidate will work at the interface of machine-learning, computational biology, and molecular biology. They will have the opportunity to collaborate with experimentalists and computational scientists across a wide range of disciplines.
Requirements- 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.
- Completed 90 ECTS relevant to the doctoral thesis project, with at least 30 ECTS in a subject closely related to the research topic.
- Experience in standard molecular biological techniques or in machine-learning.
- Very good written and oral English language skills.
- Proficiency in working with computers and programming, e.g. in Linux Shell, R, Python, Julia, etc.
The PhD student is expected to play an active role in developing this doctoral project and in the department. They will have the opportunity to work in a dynamic and collaborative environment at Umeå University.
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