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PhD Student in Deep-Learning Conifer Genomics
2 months ago
Project Overview
We are seeking a highly motivated and skilled PhD student to join our team at Umeå University in the field of deep-learning conifer genomics. The successful candidate will be involved in the development of novel deep-learning driven computational tools for efficient identification of transposable elements in large plant genomes.
Research Focus
The PhD project will focus on the development of deep-learning driven methods to advance our understanding of the complexities of transposable elements in large plant genomes. The 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 be eligible for this position, the applicant must have qualifications equivalent to a completed degree at second-cycle level, including at least 60 ECTS credits at second-cycle level. The successful candidate must have completed 90 ECTS relevant to the doctoral thesis project, with at least 30 ECTS in a subject closely related to the research topic.
Desirable Skills and Knowledge
The following skills and knowledge are highly desirable for this position:
- 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.
Application Process
The PhD student is expected to play an active role in developing this doctoral project and in the department. The assessments of the applicants are based on their qualifications and presumed ability to take part in doctoral education.
